diff --git a/data/evaluations/Scene5/aruco_positions_initial.json b/data/evaluations/Scene5/aruco_positions_initial.json
index 8ccb8d9..0ea79c6 100644
--- a/data/evaluations/Scene5/aruco_positions_initial.json
+++ b/data/evaluations/Scene5/aruco_positions_initial.json
@@ -1,782 +1,1076 @@
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diff --git a/data/evaluations/Scene5/aruco_positions_optimized.json b/data/evaluations/Scene5/aruco_positions_optimized.json
index 75033cb..13d25d5 100644
--- a/data/evaluations/Scene5/aruco_positions_optimized.json
+++ b/data/evaluations/Scene5/aruco_positions_optimized.json
@@ -1,781 +1,1075 @@
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diff --git a/data/evaluations/Scene5/render_a_aruco_detection.json b/data/evaluations/Scene5/render_a_aruco_detection.json
index 77abbc6..a582a1d 100644
--- a/data/evaluations/Scene5/render_a_aruco_detection.json
+++ b/data/evaluations/Scene5/render_a_aruco_detection.json
@@ -1,6 +1,6 @@
{
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@@ -46,7 +46,7 @@
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diff --git a/data/evaluations/Scene5/render_a_camera_pose.json b/data/evaluations/Scene5/render_a_camera_pose.json
index 28acb8d..3bfdf69 100644
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diff --git a/data/evaluations/Scene5/render_b_aruco_detection.json b/data/evaluations/Scene5/render_b_aruco_detection.json
index 5412818..93d94d4 100644
--- a/data/evaluations/Scene5/render_b_aruco_detection.json
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diff --git a/data/evaluations/Scene5/render_b_camera_pose.json b/data/evaluations/Scene5/render_b_camera_pose.json
index 0bb389d..2b6b246 100644
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diff --git a/data/evaluations/Scene5/render_c_aruco_detection.json b/data/evaluations/Scene5/render_c_aruco_detection.json
index 0cbbdb8..27edd28 100644
--- a/data/evaluations/Scene5/render_c_aruco_detection.json
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diff --git a/data/evaluations/Scene5/render_c_camera_pose.json b/data/evaluations/Scene5/render_c_camera_pose.json
index 2247fa3..5aa5b16 100644
--- a/data/evaluations/Scene5/render_c_camera_pose.json
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diff --git a/data/evaluations/Scene5/render_d_aruco_detection.json b/data/evaluations/Scene5/render_d_aruco_detection.json
index 2cda55b..842385f 100644
--- a/data/evaluations/Scene5/render_d_aruco_detection.json
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diff --git a/data/evaluations/Scene5/render_d_camera_pose.json b/data/evaluations/Scene5/render_d_camera_pose.json
index 8d3d7f8..6300601 100644
--- a/data/evaluations/Scene5/render_d_camera_pose.json
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diff --git a/data/evaluations/Scene5/render_e_aruco_detection.json b/data/evaluations/Scene5/render_e_aruco_detection.json
index 2b4ad00..6fcbce2 100644
--- a/data/evaluations/Scene5/render_e_aruco_detection.json
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diff --git a/data/evaluations/Scene5/render_e_camera_pose.json b/data/evaluations/Scene5/render_e_camera_pose.json
index 253b035..d2eaf94 100644
--- a/data/evaluations/Scene5/render_e_camera_pose.json
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diff --git a/data/evaluations/Scene5/render_f_aruco_detection.json b/data/evaluations/Scene5/render_f_aruco_detection.json
index 1c6bffc..bfe29a0 100644
--- a/data/evaluations/Scene5/render_f_aruco_detection.json
+++ b/data/evaluations/Scene5/render_f_aruco_detection.json
@@ -1,6 +1,6 @@
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diff --git a/data/evaluations/Scene5/render_f_camera_pose.json b/data/evaluations/Scene5/render_f_camera_pose.json
index c262b67..e1103a1 100644
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diff --git a/data/evaluations/Scene5/render_g_aruco_detection.json b/data/evaluations/Scene5/render_g_aruco_detection.json
index 4b392ec..af90a66 100644
--- a/data/evaluations/Scene5/render_g_aruco_detection.json
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diff --git a/data/evaluations/Scene5/render_g_camera_pose.json b/data/evaluations/Scene5/render_g_camera_pose.json
index dfb1cbe..280b94a 100644
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diff --git a/data/evaluations/Scene5/robot_state.json b/data/evaluations/Scene5/robot_state.json
index fa4e74d..0d90b79 100644
--- a/data/evaluations/Scene5/robot_state.json
+++ b/data/evaluations/Scene5/robot_state.json
@@ -1,58 +1,58 @@
{
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@@ -78,217 +78,217 @@
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\ No newline at end of file
diff --git a/data/evaluations/Scene7/aruco_positions_initial.json b/data/evaluations/Scene7/aruco_positions_initial.json
new file mode 100644
index 0000000..fb6c347
--- /dev/null
+++ b/data/evaluations/Scene7/aruco_positions_initial.json
@@ -0,0 +1,1076 @@
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diff --git a/data/evaluations/Scene7/aruco_positions_optimized.json b/data/evaluations/Scene7/aruco_positions_optimized.json
new file mode 100644
index 0000000..2b71557
--- /dev/null
+++ b/data/evaluations/Scene7/aruco_positions_optimized.json
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diff --git a/data/evaluations/Scene7/render_a_aruco_detection.json b/data/evaluations/Scene7/render_a_aruco_detection.json
new file mode 100644
index 0000000..c4702f3
--- /dev/null
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diff --git a/data/evaluations/Scene7/render_a_camera_pose.json b/data/evaluations/Scene7/render_a_camera_pose.json
new file mode 100644
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diff --git a/data/evaluations/Scene7/render_b_aruco_detection.json b/data/evaluations/Scene7/render_b_aruco_detection.json
new file mode 100644
index 0000000..a836ac0
--- /dev/null
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diff --git a/data/evaluations/Scene7/render_c_aruco_detection.json b/data/evaluations/Scene7/render_c_aruco_detection.json
new file mode 100644
index 0000000..424bb19
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diff --git a/data/evaluations/Scene7/render_c_camera_pose.json b/data/evaluations/Scene7/render_c_camera_pose.json
new file mode 100644
index 0000000..74a025b
--- /dev/null
+++ b/data/evaluations/Scene7/render_c_camera_pose.json
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\ No newline at end of file
diff --git a/data/evaluations/Scene7/render_d_aruco_detection.json b/data/evaluations/Scene7/render_d_aruco_detection.json
new file mode 100644
index 0000000..c74a1a6
--- /dev/null
+++ b/data/evaluations/Scene7/render_d_aruco_detection.json
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diff --git a/data/evaluations/Scene7/render_d_camera_pose.json b/data/evaluations/Scene7/render_d_camera_pose.json
new file mode 100644
index 0000000..8a1adfe
--- /dev/null
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diff --git a/data/evaluations/Scene7/render_e_aruco_detection.json b/data/evaluations/Scene7/render_e_aruco_detection.json
new file mode 100644
index 0000000..ea36953
--- /dev/null
+++ b/data/evaluations/Scene7/render_e_aruco_detection.json
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diff --git a/data/evaluations/Scene7/render_f_aruco_detection.json b/data/evaluations/Scene7/render_f_aruco_detection.json
new file mode 100644
index 0000000..c296bbc
--- /dev/null
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diff --git a/data/evaluations/Scene7/render_f_camera_pose.json b/data/evaluations/Scene7/render_f_camera_pose.json
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diff --git a/data/evaluations/Scene7/render_g_aruco_detection.json b/data/evaluations/Scene7/render_g_aruco_detection.json
new file mode 100644
index 0000000..b03d77a
--- /dev/null
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diff --git a/data/evaluations/Scene7/render_g_camera_pose.json b/data/evaluations/Scene7/render_g_camera_pose.json
new file mode 100644
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diff --git a/data/evaluations/Scene7/robot_state.json b/data/evaluations/Scene7/robot_state.json
new file mode 100644
index 0000000..8f348d2
--- /dev/null
+++ b/data/evaluations/Scene7/robot_state.json
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+ ]
+}
\ No newline at end of file
diff --git a/data/robot/robot.json b/data/robot/robot.json
index a6d5358..48cea29 100644
--- a/data/robot/robot.json
+++ b/data/robot/robot.json
@@ -14,8 +14,8 @@
"MarkerSize": 0.025
},
"renderingInfo": {
- "width": 1280,
- "height": 720,
+ "width": 4896,
+ "height": 3264,
"cameraPosition__1": [
-10,
-800,
@@ -42,9 +42,9 @@
500
],
"cameraPosition": [
- 500,
-300,
- 1400
+ -800,
+ 800
],
"cameraPosition_c": [
600,
@@ -52,8 +52,8 @@
600
],
"cameraTarget": [
- 200,
- -200,
+ 210,
+ -100,
180
],
"cameraUpVector": [
@@ -91,7 +91,7 @@
"lensDirt": true,
"lensDirtStrength": 0.08,
"dofEnabled": true,
- "dofFStop": 11.0,
+ "dofFStop": 5.6,
"arucoDust": false,
"arucoDustStrength": 5e-05,
"localizedBlur": false,
@@ -204,13 +204,13 @@
"e": 1
},
"defaultPosition": {
- "x": 30,
+ "x": 60,
"y": -2,
"z": 95,
- "a": 20,
- "b": 23,
+ "a": 200,
+ "b": 60,
"c": 9,
- "e": 9
+ "e": 8
},
"recognized": {
"x": null,
diff --git a/data/simulation/Scene5/pose.json b/data/simulation/Scene5/pose.json
index f05cc2c..283611e 100644
--- a/data/simulation/Scene5/pose.json
+++ b/data/simulation/Scene5/pose.json
@@ -9,6 +9,11 @@
"c": 91,
"e": 10
},
+ "rendering": {
+ "width": 1280,
+ "height": 720,
+ "dofFStop": 11.0
+ },
"camera_positions": {
"a": [
-300,
@@ -45,5 +50,42 @@
200,
1400
]
+ },
+ "camera_targets": {
+ "a": [
+ 210,
+ -100,
+ 180
+ ],
+ "b": [
+ 310,
+ -80,
+ 180
+ ],
+ "c": [
+ 210,
+ -100,
+ 150
+ ],
+ "d": [
+ 210,
+ -100,
+ 150
+ ],
+ "e": [
+ 210,
+ -100,
+ 50
+ ],
+ "f": [
+ 200,
+ -200,
+ 180
+ ],
+ "g": [
+ 200,
+ -200,
+ 180
+ ]
}
}
\ No newline at end of file
diff --git a/data/simulation/Scene5/render_a.json b/data/simulation/Scene5/render_a.json
index f7ac0e7..13f7433 100644
--- a/data/simulation/Scene5/render_a.json
+++ b/data/simulation/Scene5/render_a.json
@@ -2001,6 +2001,84 @@
0.8290374279022217
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{
"name": "Arm2_marker_120",
"id": 120,
diff --git a/data/simulation/Scene5/render_a.npz b/data/simulation/Scene5/render_a.npz
index 6243600..b10ee26 100644
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diff --git a/data/simulation/Scene5/render_a.png b/data/simulation/Scene5/render_a.png
index a6a5258..306aba5 100644
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diff --git a/data/simulation/Scene5/render_b.json b/data/simulation/Scene5/render_b.json
index f7ac0e7..13f7433 100644
--- a/data/simulation/Scene5/render_b.json
+++ b/data/simulation/Scene5/render_b.json
@@ -2001,6 +2001,84 @@
0.8290374279022217
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+ "id": 248,
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+ "link": "Ellbow",
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{
"name": "Arm2_marker_120",
"id": 120,
diff --git a/data/simulation/Scene5/render_b.npz b/data/simulation/Scene5/render_b.npz
index 6243600..b10ee26 100644
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diff --git a/data/simulation/Scene5/render_b.png b/data/simulation/Scene5/render_b.png
index 849dd44..1ade59b 100644
Binary files a/data/simulation/Scene5/render_b.png and b/data/simulation/Scene5/render_b.png differ
diff --git a/data/simulation/Scene5/render_c.json b/data/simulation/Scene5/render_c.json
index f7ac0e7..13f7433 100644
--- a/data/simulation/Scene5/render_c.json
+++ b/data/simulation/Scene5/render_c.json
@@ -2001,6 +2001,84 @@
0.8290374279022217
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+ {
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+ "id": 248,
+ "link": "Ellbow",
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+ {
+ "name": "aruco_232",
+ "id": 232,
+ "link": "Ellbow",
+ "position_m": [
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{
"name": "Arm2_marker_120",
"id": 120,
diff --git a/data/simulation/Scene5/render_c.npz b/data/simulation/Scene5/render_c.npz
index 6243600..b10ee26 100644
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diff --git a/data/simulation/Scene5/render_c.png b/data/simulation/Scene5/render_c.png
index 98c952a..ae571d9 100644
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diff --git a/data/simulation/Scene5/render_d.json b/data/simulation/Scene5/render_d.json
index f7ac0e7..13f7433 100644
--- a/data/simulation/Scene5/render_d.json
+++ b/data/simulation/Scene5/render_d.json
@@ -2001,6 +2001,84 @@
0.8290374279022217
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+ {
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+ "id": 248,
+ "link": "Ellbow",
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+ "link": "Ellbow",
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{
"name": "Arm2_marker_120",
"id": 120,
diff --git a/data/simulation/Scene5/render_d.npz b/data/simulation/Scene5/render_d.npz
index 6243600..b10ee26 100644
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diff --git a/data/simulation/Scene5/render_d.png b/data/simulation/Scene5/render_d.png
index 432a7d9..62a0daa 100644
Binary files a/data/simulation/Scene5/render_d.png and b/data/simulation/Scene5/render_d.png differ
diff --git a/data/simulation/Scene5/render_e.json b/data/simulation/Scene5/render_e.json
index f7ac0e7..13f7433 100644
--- a/data/simulation/Scene5/render_e.json
+++ b/data/simulation/Scene5/render_e.json
@@ -2001,6 +2001,84 @@
0.8290374279022217
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+ {
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+ "id": 248,
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+ {
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+ ]
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+ {
+ "name": "aruco_231",
+ "id": 231,
+ "link": "Ellbow",
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+ ]
+ },
{
"name": "Arm2_marker_120",
"id": 120,
diff --git a/data/simulation/Scene5/render_e.npz b/data/simulation/Scene5/render_e.npz
index 6243600..b10ee26 100644
Binary files a/data/simulation/Scene5/render_e.npz and b/data/simulation/Scene5/render_e.npz differ
diff --git a/data/simulation/Scene5/render_e.png b/data/simulation/Scene5/render_e.png
index e911335..585ca27 100644
Binary files a/data/simulation/Scene5/render_e.png and b/data/simulation/Scene5/render_e.png differ
diff --git a/data/simulation/Scene5/render_f.json b/data/simulation/Scene5/render_f.json
index f7ac0e7..13f7433 100644
--- a/data/simulation/Scene5/render_f.json
+++ b/data/simulation/Scene5/render_f.json
@@ -2001,6 +2001,84 @@
0.8290374279022217
]
},
+ {
+ "name": "aruco_248",
+ "id": 248,
+ "link": "Ellbow",
+ "position_m": [
+ 0.3425000309944153,
+ 0.11041221022605896,
+ 0.28096017241477966
+ ],
+ "position_mm": [
+ 342.5000309944153,
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+ 280.96017241477966
+ ],
+ "rotation_quaternion": [
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+ ],
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+ -5.960463766996327e-08,
+ 0.5591931939125061,
+ -0.8290373086929321
+ ]
+ },
+ {
+ "name": "aruco_232",
+ "id": 232,
+ "link": "Ellbow",
+ "position_m": [
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+ 0.12541037797927856,
+ 0.3036169409751892
+ ],
+ "position_mm": [
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+ "rotation_quaternion": [
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+ "normal": [
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+ 0.9816272258758545,
+ -0.19080877304077148
+ ]
+ },
+ {
+ "name": "aruco_231",
+ "id": 231,
+ "link": "Ellbow",
+ "position_m": [
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+ 0.09733490645885468,
+ 0.3452405333518982
+ ],
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+ "rotation_quaternion": [
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+ ],
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+ 0.19080865383148193,
+ 0.9816272258758545
+ ]
+ },
{
"name": "Arm2_marker_120",
"id": 120,
diff --git a/data/simulation/Scene5/render_f.npz b/data/simulation/Scene5/render_f.npz
index 6243600..b10ee26 100644
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diff --git a/data/simulation/Scene5/render_f.png b/data/simulation/Scene5/render_f.png
index fb730dc..f83c47c 100644
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diff --git a/data/simulation/Scene5/render_g.json b/data/simulation/Scene5/render_g.json
index f7ac0e7..13f7433 100644
--- a/data/simulation/Scene5/render_g.json
+++ b/data/simulation/Scene5/render_g.json
@@ -2001,6 +2001,84 @@
0.8290374279022217
]
},
+ {
+ "name": "aruco_248",
+ "id": 248,
+ "link": "Ellbow",
+ "position_m": [
+ 0.3425000309944153,
+ 0.11041221022605896,
+ 0.28096017241477966
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+ "rotation_quaternion": [
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+ ],
+ "normal": [
+ -5.960463766996327e-08,
+ 0.5591931939125061,
+ -0.8290373086929321
+ ]
+ },
+ {
+ "name": "aruco_232",
+ "id": 232,
+ "link": "Ellbow",
+ "position_m": [
+ 0.3800000250339508,
+ 0.12541037797927856,
+ 0.3036169409751892
+ ],
+ "position_mm": [
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+ "rotation_quaternion": [
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+ -0.0
+ ],
+ "normal": [
+ 0.0,
+ 0.9816272258758545,
+ -0.19080877304077148
+ ]
+ },
+ {
+ "name": "aruco_231",
+ "id": 231,
+ "link": "Ellbow",
+ "position_m": [
+ 0.3800000250339508,
+ 0.09733490645885468,
+ 0.3452405333518982
+ ],
+ "position_mm": [
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+ ],
+ "rotation_quaternion": [
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+ -0.09584558010101318,
+ 0.0,
+ 0.0
+ ],
+ "normal": [
+ 0.0,
+ 0.19080865383148193,
+ 0.9816272258758545
+ ]
+ },
{
"name": "Arm2_marker_120",
"id": 120,
diff --git a/data/simulation/Scene5/render_g.npz b/data/simulation/Scene5/render_g.npz
index 6243600..b10ee26 100644
Binary files a/data/simulation/Scene5/render_g.npz and b/data/simulation/Scene5/render_g.npz differ
diff --git a/data/simulation/Scene5/render_g.png b/data/simulation/Scene5/render_g.png
index b99e926..e6c1656 100644
Binary files a/data/simulation/Scene5/render_g.png and b/data/simulation/Scene5/render_g.png differ
diff --git a/data/simulation/Scene6/pose.json b/data/simulation/Scene6/pose.json
index 449bf42..c25c898 100644
--- a/data/simulation/Scene6/pose.json
+++ b/data/simulation/Scene6/pose.json
@@ -9,6 +9,11 @@
"c": 9,
"e": 3
},
+ "rendering": {
+ "width": 1280,
+ "height": 720,
+ "dofFStop": 11.0
+ },
"camera_positions": {
"a": [
-300,
@@ -45,5 +50,42 @@
200,
1400
]
+ },
+ "camera_targets": {
+ "a": [
+ 210,
+ -100,
+ 180
+ ],
+ "b": [
+ 310,
+ -80,
+ 180
+ ],
+ "c": [
+ 210,
+ -100,
+ 150
+ ],
+ "d": [
+ 210,
+ -100,
+ 150
+ ],
+ "e": [
+ 210,
+ -100,
+ 50
+ ],
+ "f": [
+ 200,
+ -200,
+ 180
+ ],
+ "g": [
+ 200,
+ -200,
+ 180
+ ]
}
}
\ No newline at end of file
diff --git a/data/simulation/Scene6/render_a.json b/data/simulation/Scene6/render_a.json
index c939fba..ca2dfe5 100644
--- a/data/simulation/Scene6/render_a.json
+++ b/data/simulation/Scene6/render_a.json
@@ -2001,6 +2001,84 @@
-0.17364829778671265
]
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+ {
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+ "id": 248,
+ "link": "Ellbow",
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{
"name": "Arm2_marker_120",
"id": 120,
diff --git a/data/simulation/Scene6/render_a.npz b/data/simulation/Scene6/render_a.npz
index 6243600..b10ee26 100644
Binary files a/data/simulation/Scene6/render_a.npz and b/data/simulation/Scene6/render_a.npz differ
diff --git a/data/simulation/Scene6/render_a.png b/data/simulation/Scene6/render_a.png
index 2ea3955..20e6f9e 100644
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diff --git a/data/simulation/Scene6/render_b.json b/data/simulation/Scene6/render_b.json
index c939fba..ca2dfe5 100644
--- a/data/simulation/Scene6/render_b.json
+++ b/data/simulation/Scene6/render_b.json
@@ -2001,6 +2001,84 @@
-0.17364829778671265
]
},
+ {
+ "name": "aruco_248",
+ "id": 248,
+ "link": "Ellbow",
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{
"name": "Arm2_marker_120",
"id": 120,
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index 6243600..b10ee26 100644
Binary files a/data/simulation/Scene6/render_b.npz and b/data/simulation/Scene6/render_b.npz differ
diff --git a/data/simulation/Scene6/render_b.png b/data/simulation/Scene6/render_b.png
index cba60bb..6f7c27c 100644
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diff --git a/data/simulation/Scene6/render_c.json b/data/simulation/Scene6/render_c.json
index c939fba..ca2dfe5 100644
--- a/data/simulation/Scene6/render_c.json
+++ b/data/simulation/Scene6/render_c.json
@@ -2001,6 +2001,84 @@
-0.17364829778671265
]
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+ {
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+ {
+ "name": "aruco_231",
+ "id": 231,
+ "link": "Ellbow",
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{
"name": "Arm2_marker_120",
"id": 120,
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index 6243600..b10ee26 100644
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diff --git a/data/simulation/Scene6/render_c.png b/data/simulation/Scene6/render_c.png
index 5d6462f..afb65de 100644
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diff --git a/data/simulation/Scene6/render_d.json b/data/simulation/Scene6/render_d.json
index c939fba..ca2dfe5 100644
--- a/data/simulation/Scene6/render_d.json
+++ b/data/simulation/Scene6/render_d.json
@@ -2001,6 +2001,84 @@
-0.17364829778671265
]
},
+ {
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+ "id": 248,
+ "link": "Ellbow",
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{
"name": "Arm2_marker_120",
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diff --git a/data/simulation/Scene6/render_d.npz b/data/simulation/Scene6/render_d.npz
index 6243600..b10ee26 100644
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diff --git a/data/simulation/Scene6/render_d.png b/data/simulation/Scene6/render_d.png
index 73134d2..25c37ad 100644
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diff --git a/data/simulation/Scene6/render_e.json b/data/simulation/Scene6/render_e.json
index c939fba..ca2dfe5 100644
--- a/data/simulation/Scene6/render_e.json
+++ b/data/simulation/Scene6/render_e.json
@@ -2001,6 +2001,84 @@
-0.17364829778671265
]
},
+ {
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+ "link": "Ellbow",
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+ ]
+ },
{
"name": "Arm2_marker_120",
"id": 120,
diff --git a/data/simulation/Scene6/render_e.npz b/data/simulation/Scene6/render_e.npz
index 6243600..b10ee26 100644
Binary files a/data/simulation/Scene6/render_e.npz and b/data/simulation/Scene6/render_e.npz differ
diff --git a/data/simulation/Scene6/render_e.png b/data/simulation/Scene6/render_e.png
index 4b4908c..ffdc17c 100644
Binary files a/data/simulation/Scene6/render_e.png and b/data/simulation/Scene6/render_e.png differ
diff --git a/data/simulation/Scene6/render_f.json b/data/simulation/Scene6/render_f.json
index c939fba..ca2dfe5 100644
--- a/data/simulation/Scene6/render_f.json
+++ b/data/simulation/Scene6/render_f.json
@@ -2001,6 +2001,84 @@
-0.17364829778671265
]
},
+ {
+ "name": "aruco_248",
+ "id": 248,
+ "link": "Ellbow",
+ "position_m": [
+ 0.24249999225139618,
+ -0.16188383102416992,
+ 0.15266956388950348
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+ "position_mm": [
+ 242.49999225139618,
+ -161.88383102416992,
+ 152.66956388950348
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+ 0.5416752099990845,
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+ 0.4545193910598755,
+ -0.5416752696037292
+ ],
+ "normal": [
+ -1.4901161193847656e-07,
+ -0.9848077297210693,
+ 0.17364828288555145
+ ]
+ },
+ {
+ "name": "aruco_232",
+ "id": 232,
+ "link": "Ellbow",
+ "position_m": [
+ 0.2800000011920929,
+ -0.15600450336933136,
+ 0.12614205479621887
+ ],
+ "position_mm": [
+ 280.0000011920929,
+ -156.00450336933136,
+ 126.14205479621887
+ ],
+ "rotation_quaternion": [
+ 0.4617486298084259,
+ 0.8870108127593994,
+ 0.0,
+ 0.0
+ ],
+ "normal": [
+ 0.0,
+ -0.8191520571708679,
+ -0.5735763907432556
+ ]
+ },
+ {
+ "name": "aruco_231",
+ "id": 231,
+ "link": "Ellbow",
+ "position_m": [
+ 0.2800000011920929,
+ -0.10656015574932098,
+ 0.11742367595434189
+ ],
+ "position_mm": [
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+ 0.5735763907432556,
+ -0.8191519975662231
+ ]
+ },
{
"name": "Arm2_marker_120",
"id": 120,
diff --git a/data/simulation/Scene6/render_f.npz b/data/simulation/Scene6/render_f.npz
index 6243600..b10ee26 100644
Binary files a/data/simulation/Scene6/render_f.npz and b/data/simulation/Scene6/render_f.npz differ
diff --git a/data/simulation/Scene6/render_f.png b/data/simulation/Scene6/render_f.png
index c2a8af3..49a8884 100644
Binary files a/data/simulation/Scene6/render_f.png and b/data/simulation/Scene6/render_f.png differ
diff --git a/data/simulation/Scene6/render_g.json b/data/simulation/Scene6/render_g.json
index c939fba..ca2dfe5 100644
--- a/data/simulation/Scene6/render_g.json
+++ b/data/simulation/Scene6/render_g.json
@@ -2001,6 +2001,84 @@
-0.17364829778671265
]
},
+ {
+ "name": "aruco_248",
+ "id": 248,
+ "link": "Ellbow",
+ "position_m": [
+ 0.24249999225139618,
+ -0.16188383102416992,
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+ -0.9848077297210693,
+ 0.17364828288555145
+ ]
+ },
+ {
+ "name": "aruco_232",
+ "id": 232,
+ "link": "Ellbow",
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+ -0.15600450336933136,
+ 0.12614205479621887
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+ -0.8191520571708679,
+ -0.5735763907432556
+ ]
+ },
+ {
+ "name": "aruco_231",
+ "id": 231,
+ "link": "Ellbow",
+ "position_m": [
+ 0.2800000011920929,
+ -0.10656015574932098,
+ 0.11742367595434189
+ ],
+ "position_mm": [
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+ "rotation_quaternion": [
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+ 0.0,
+ 0.5735763907432556,
+ -0.8191519975662231
+ ]
+ },
{
"name": "Arm2_marker_120",
"id": 120,
diff --git a/data/simulation/Scene6/render_g.npz b/data/simulation/Scene6/render_g.npz
index 6243600..b10ee26 100644
Binary files a/data/simulation/Scene6/render_g.npz and b/data/simulation/Scene6/render_g.npz differ
diff --git a/data/simulation/Scene6/render_g.png b/data/simulation/Scene6/render_g.png
index 55aa545..ca5be3b 100644
Binary files a/data/simulation/Scene6/render_g.png and b/data/simulation/Scene6/render_g.png differ
diff --git a/data/simulation/Scene7/render_f.json b/data/simulation/Scene7/render_f.json
index 81e77f6..a7fb394 100644
--- a/data/simulation/Scene7/render_f.json
+++ b/data/simulation/Scene7/render_f.json
@@ -2001,6 +2001,84 @@
-0.05233597010374069
]
},
+ {
+ "name": "aruco_248",
+ "id": 248,
+ "link": "Ellbow",
+ "position_m": [
+ 0.19249999523162842,
+ -0.17729905247688293,
+ 0.05413304641842842
+ ],
+ "position_mm": [
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+ -177.29905247688293,
+ 54.13304641842842
+ ],
+ "rotation_quaternion": [
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+ 0.4867401123046875,
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+ "normal": [
+ -2.0861628513557662e-07,
+ -0.9986295700073242,
+ 0.05233597010374069
+ ]
+ },
+ {
+ "name": "aruco_232",
+ "id": 232,
+ "link": "Ellbow",
+ "position_m": [
+ 0.23000000417232513,
+ -0.16823066771030426,
+ 0.02851978689432144
+ ],
+ "position_mm": [
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+ ],
+ "rotation_quaternion": [
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+ ],
+ "normal": [
+ 0.0,
+ -0.7431448698043823,
+ -0.6691306233406067
+ ]
+ },
+ {
+ "name": "aruco_231",
+ "id": 231,
+ "link": "Ellbow",
+ "position_m": [
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+ -0.1180923730134964,
+ 0.02589215338230133
+ ],
+ "position_mm": [
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+ ],
+ "rotation_quaternion": [
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+ ],
+ "normal": [
+ 0.0,
+ 0.6691306829452515,
+ -0.7431447505950928
+ ]
+ },
{
"name": "Arm2_marker_120",
"id": 120,
diff --git a/data/simulation/Scene7/render_f.npz b/data/simulation/Scene7/render_f.npz
index 6243600..b10ee26 100644
Binary files a/data/simulation/Scene7/render_f.npz and b/data/simulation/Scene7/render_f.npz differ
diff --git a/data/simulation/Scene7/render_f.png b/data/simulation/Scene7/render_f.png
index 9297a21..ab309a1 100644
Binary files a/data/simulation/Scene7/render_f.png and b/data/simulation/Scene7/render_f.png differ
diff --git a/data/simulation/Scene7/render_g.json b/data/simulation/Scene7/render_g.json
index 81e77f6..a7fb394 100644
--- a/data/simulation/Scene7/render_g.json
+++ b/data/simulation/Scene7/render_g.json
@@ -2001,6 +2001,84 @@
-0.05233597010374069
]
},
+ {
+ "name": "aruco_248",
+ "id": 248,
+ "link": "Ellbow",
+ "position_m": [
+ 0.19249999523162842,
+ -0.17729905247688293,
+ 0.05413304641842842
+ ],
+ "position_mm": [
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+ 54.13304641842842
+ ],
+ "rotation_quaternion": [
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+ 0.4867401123046875,
+ -0.512917160987854
+ ],
+ "normal": [
+ -2.0861628513557662e-07,
+ -0.9986295700073242,
+ 0.05233597010374069
+ ]
+ },
+ {
+ "name": "aruco_232",
+ "id": 232,
+ "link": "Ellbow",
+ "position_m": [
+ 0.23000000417232513,
+ -0.16823066771030426,
+ 0.02851978689432144
+ ],
+ "position_mm": [
+ 230.00000417232513,
+ -168.23066771030426,
+ 28.51978689432144
+ ],
+ "rotation_quaternion": [
+ 0.4067366421222687,
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+ 0.0,
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+ ],
+ "normal": [
+ 0.0,
+ -0.7431448698043823,
+ -0.6691306233406067
+ ]
+ },
+ {
+ "name": "aruco_231",
+ "id": 231,
+ "link": "Ellbow",
+ "position_m": [
+ 0.23000000417232513,
+ -0.1180923730134964,
+ 0.02589215338230133
+ ],
+ "position_mm": [
+ 230.00000417232513,
+ -118.0923730134964,
+ 25.89215338230133
+ ],
+ "rotation_quaternion": [
+ 0.35836800932884216,
+ -0.9335803985595703,
+ -0.0,
+ -0.0
+ ],
+ "normal": [
+ 0.0,
+ 0.6691306829452515,
+ -0.7431447505950928
+ ]
+ },
{
"name": "Arm2_marker_120",
"id": 120,
diff --git a/data/simulation/Scene7/render_g.npz b/data/simulation/Scene7/render_g.npz
index 6243600..b10ee26 100644
Binary files a/data/simulation/Scene7/render_g.npz and b/data/simulation/Scene7/render_g.npz differ
diff --git a/data/simulation/Scene7/render_g.png b/data/simulation/Scene7/render_g.png
index c67bfe6..fe59a01 100644
Binary files a/data/simulation/Scene7/render_g.png and b/data/simulation/Scene7/render_g.png differ
diff --git a/data/simulation/Scene9/pose.json b/data/simulation/Scene9/pose.json
index 8b0122a..76acf59 100644
--- a/data/simulation/Scene9/pose.json
+++ b/data/simulation/Scene9/pose.json
@@ -9,6 +9,11 @@
"c": 9,
"e": 8
},
+ "rendering": {
+ "width": 1280,
+ "height": 720,
+ "dofFStop": 11.0
+ },
"camera_positions": {
"a": [
-300,
@@ -45,5 +50,42 @@
200,
1400
]
+ },
+ "camera_targets": {
+ "a": [
+ 210,
+ -100,
+ 180
+ ],
+ "b": [
+ 310,
+ -80,
+ 180
+ ],
+ "c": [
+ 210,
+ -100,
+ 150
+ ],
+ "d": [
+ 210,
+ -100,
+ 150
+ ],
+ "e": [
+ 210,
+ -100,
+ 50
+ ],
+ "f": [
+ 200,
+ -200,
+ 180
+ ],
+ "g": [
+ 200,
+ -200,
+ 180
+ ]
}
}
\ No newline at end of file
diff --git a/data/simulation/Scene9/render_a.json b/data/simulation/Scene9/render_a.json
index 6870bb5..e812ff0 100644
--- a/data/simulation/Scene9/render_a.json
+++ b/data/simulation/Scene9/render_a.json
@@ -2001,6 +2001,84 @@
-0.05233597010374069
]
},
+ {
+ "name": "aruco_248",
+ "id": 248,
+ "link": "Ellbow",
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+ {
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+ "link": "Ellbow",
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+ {
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+ "link": "Ellbow",
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+ -0.7431447505950928
+ ]
+ },
{
"name": "Arm2_marker_120",
"id": 120,
diff --git a/data/simulation/Scene9/render_a.npz b/data/simulation/Scene9/render_a.npz
index 6243600..b10ee26 100644
Binary files a/data/simulation/Scene9/render_a.npz and b/data/simulation/Scene9/render_a.npz differ
diff --git a/data/simulation/Scene9/render_a.png b/data/simulation/Scene9/render_a.png
index 48ff985..1fc230e 100644
Binary files a/data/simulation/Scene9/render_a.png and b/data/simulation/Scene9/render_a.png differ
diff --git a/data/simulation/Scene9/render_b.json b/data/simulation/Scene9/render_b.json
index 6870bb5..e812ff0 100644
--- a/data/simulation/Scene9/render_b.json
+++ b/data/simulation/Scene9/render_b.json
@@ -2001,6 +2001,84 @@
-0.05233597010374069
]
},
+ {
+ "name": "aruco_248",
+ "id": 248,
+ "link": "Ellbow",
+ "position_m": [
+ 0.2224999964237213,
+ -0.17729905247688293,
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+ ]
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+ "link": "Ellbow",
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+ },
+ {
+ "name": "aruco_231",
+ "id": 231,
+ "link": "Ellbow",
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+ -0.1180923730134964,
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+ -0.7431447505950928
+ ]
+ },
{
"name": "Arm2_marker_120",
"id": 120,
diff --git a/data/simulation/Scene9/render_b.npz b/data/simulation/Scene9/render_b.npz
index 6243600..b10ee26 100644
Binary files a/data/simulation/Scene9/render_b.npz and b/data/simulation/Scene9/render_b.npz differ
diff --git a/data/simulation/Scene9/render_b.png b/data/simulation/Scene9/render_b.png
index 7ec270a..2749e78 100644
Binary files a/data/simulation/Scene9/render_b.png and b/data/simulation/Scene9/render_b.png differ
diff --git a/data/simulation/Scene9/render_c.json b/data/simulation/Scene9/render_c.json
index 6870bb5..e812ff0 100644
--- a/data/simulation/Scene9/render_c.json
+++ b/data/simulation/Scene9/render_c.json
@@ -2001,6 +2001,84 @@
-0.05233597010374069
]
},
+ {
+ "name": "aruco_248",
+ "id": 248,
+ "link": "Ellbow",
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+ "normal": [
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+ ]
+ },
+ {
+ "name": "aruco_232",
+ "id": 232,
+ "link": "Ellbow",
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+ -0.16823066771030426,
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+ },
+ {
+ "name": "aruco_231",
+ "id": 231,
+ "link": "Ellbow",
+ "position_m": [
+ 0.25999999046325684,
+ -0.1180923730134964,
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+ "position_mm": [
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{
"name": "Arm2_marker_120",
"id": 120,
diff --git a/data/simulation/Scene9/render_c.npz b/data/simulation/Scene9/render_c.npz
index 6243600..b10ee26 100644
Binary files a/data/simulation/Scene9/render_c.npz and b/data/simulation/Scene9/render_c.npz differ
diff --git a/data/simulation/Scene9/render_c.png b/data/simulation/Scene9/render_c.png
index 310cc37..69d0bfe 100644
Binary files a/data/simulation/Scene9/render_c.png and b/data/simulation/Scene9/render_c.png differ
diff --git a/data/simulation/Scene9/render_d.json b/data/simulation/Scene9/render_d.json
index 6870bb5..e812ff0 100644
--- a/data/simulation/Scene9/render_d.json
+++ b/data/simulation/Scene9/render_d.json
@@ -2001,6 +2001,84 @@
-0.05233597010374069
]
},
+ {
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+ "id": 248,
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+ "link": "Ellbow",
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+ ]
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{
"name": "Arm2_marker_120",
"id": 120,
diff --git a/data/simulation/Scene9/render_d.npz b/data/simulation/Scene9/render_d.npz
index 6243600..b10ee26 100644
Binary files a/data/simulation/Scene9/render_d.npz and b/data/simulation/Scene9/render_d.npz differ
diff --git a/data/simulation/Scene9/render_d.png b/data/simulation/Scene9/render_d.png
index f6fe25d..418f65d 100644
Binary files a/data/simulation/Scene9/render_d.png and b/data/simulation/Scene9/render_d.png differ
diff --git a/data/simulation/Scene9/render_e.json b/data/simulation/Scene9/render_e.json
index 6870bb5..e812ff0 100644
--- a/data/simulation/Scene9/render_e.json
+++ b/data/simulation/Scene9/render_e.json
@@ -2001,6 +2001,84 @@
-0.05233597010374069
]
},
+ {
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+ "id": 248,
+ "link": "Ellbow",
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+ ]
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+ "id": 232,
+ "link": "Ellbow",
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+ {
+ "name": "aruco_231",
+ "id": 231,
+ "link": "Ellbow",
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+ -0.1180923730134964,
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+ -0.7431447505950928
+ ]
+ },
{
"name": "Arm2_marker_120",
"id": 120,
diff --git a/data/simulation/Scene9/render_e.npz b/data/simulation/Scene9/render_e.npz
index 6243600..b10ee26 100644
Binary files a/data/simulation/Scene9/render_e.npz and b/data/simulation/Scene9/render_e.npz differ
diff --git a/data/simulation/Scene9/render_e.png b/data/simulation/Scene9/render_e.png
index 2bf4698..3ce866b 100644
Binary files a/data/simulation/Scene9/render_e.png and b/data/simulation/Scene9/render_e.png differ
diff --git a/data/simulation/Scene9/render_f.json b/data/simulation/Scene9/render_f.json
index 6870bb5..e812ff0 100644
--- a/data/simulation/Scene9/render_f.json
+++ b/data/simulation/Scene9/render_f.json
@@ -2001,6 +2001,84 @@
-0.05233597010374069
]
},
+ {
+ "name": "aruco_248",
+ "id": 248,
+ "link": "Ellbow",
+ "position_m": [
+ 0.2224999964237213,
+ -0.17729905247688293,
+ 0.05413304641842842
+ ],
+ "position_mm": [
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+ ],
+ "rotation_quaternion": [
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+ 0.4867401123046875,
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+ ],
+ "normal": [
+ -2.0861628513557662e-07,
+ -0.9986295700073242,
+ 0.05233597010374069
+ ]
+ },
+ {
+ "name": "aruco_232",
+ "id": 232,
+ "link": "Ellbow",
+ "position_m": [
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+ -0.16823066771030426,
+ 0.02851978689432144
+ ],
+ "position_mm": [
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+ "rotation_quaternion": [
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+ ],
+ "normal": [
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+ -0.6691306233406067
+ ]
+ },
+ {
+ "name": "aruco_231",
+ "id": 231,
+ "link": "Ellbow",
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+ -0.1180923730134964,
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+ "position_mm": [
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+ -0.7431447505950928
+ ]
+ },
{
"name": "Arm2_marker_120",
"id": 120,
diff --git a/data/simulation/Scene9/render_f.npz b/data/simulation/Scene9/render_f.npz
index 6243600..b10ee26 100644
Binary files a/data/simulation/Scene9/render_f.npz and b/data/simulation/Scene9/render_f.npz differ
diff --git a/data/simulation/Scene9/render_f.png b/data/simulation/Scene9/render_f.png
index 3876eb3..0763ded 100644
Binary files a/data/simulation/Scene9/render_f.png and b/data/simulation/Scene9/render_f.png differ
diff --git a/data/simulation/Scene9/render_g.json b/data/simulation/Scene9/render_g.json
index 6870bb5..e812ff0 100644
--- a/data/simulation/Scene9/render_g.json
+++ b/data/simulation/Scene9/render_g.json
@@ -2001,6 +2001,84 @@
-0.05233597010374069
]
},
+ {
+ "name": "aruco_248",
+ "id": 248,
+ "link": "Ellbow",
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+ -0.17729905247688293,
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+ ],
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+ ],
+ "normal": [
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+ -0.9986295700073242,
+ 0.05233597010374069
+ ]
+ },
+ {
+ "name": "aruco_232",
+ "id": 232,
+ "link": "Ellbow",
+ "position_m": [
+ 0.25999999046325684,
+ -0.16823066771030426,
+ 0.02851978689432144
+ ],
+ "position_mm": [
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+ -168.23066771030426,
+ 28.51978689432144
+ ],
+ "rotation_quaternion": [
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+ 0.0,
+ 0.0
+ ],
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+ -0.7431448698043823,
+ -0.6691306233406067
+ ]
+ },
+ {
+ "name": "aruco_231",
+ "id": 231,
+ "link": "Ellbow",
+ "position_m": [
+ 0.25999999046325684,
+ -0.1180923730134964,
+ 0.02589215338230133
+ ],
+ "position_mm": [
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+ -118.0923730134964,
+ 25.89215338230133
+ ],
+ "rotation_quaternion": [
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+ -0.9335803985595703,
+ -0.0,
+ -0.0
+ ],
+ "normal": [
+ 0.0,
+ 0.6691306829452515,
+ -0.7431447505950928
+ ]
+ },
{
"name": "Arm2_marker_120",
"id": 120,
diff --git a/data/simulation/Scene9/render_g.npz b/data/simulation/Scene9/render_g.npz
index 6243600..b10ee26 100644
Binary files a/data/simulation/Scene9/render_g.npz and b/data/simulation/Scene9/render_g.npz differ
diff --git a/data/simulation/Scene9/render_g.png b/data/simulation/Scene9/render_g.png
index 59234be..db97787 100644
Binary files a/data/simulation/Scene9/render_g.png and b/data/simulation/Scene9/render_g.png differ
diff --git a/data/simulation/Scene9a/pose.json b/data/simulation/Scene9a/pose.json
index 04c0abe..9974214 100644
--- a/data/simulation/Scene9a/pose.json
+++ b/data/simulation/Scene9a/pose.json
@@ -9,6 +9,11 @@
"c": 9,
"e": 8
},
+ "rendering": {
+ "width": 1440,
+ "height": 1080,
+ "dofFStop": 11
+ },
"camera_positions": {
"a": [
-300,
@@ -45,5 +50,42 @@
200,
1400
]
+ },
+ "camera_targets": {
+ "a": [
+ 210,
+ -100,
+ 180
+ ],
+ "b": [
+ 310,
+ -80,
+ 180
+ ],
+ "c": [
+ 210,
+ -100,
+ 150
+ ],
+ "d": [
+ 210,
+ -100,
+ 150
+ ],
+ "e": [
+ 210,
+ -100,
+ 50
+ ],
+ "f": [
+ 200,
+ -200,
+ 180
+ ],
+ "g": [
+ 200,
+ -200,
+ 180
+ ]
}
}
\ No newline at end of file
diff --git a/data/simulation/Scene9a/render_a.json b/data/simulation/Scene9a/render_a.json
index 6870bb5..e812ff0 100644
--- a/data/simulation/Scene9a/render_a.json
+++ b/data/simulation/Scene9a/render_a.json
@@ -2001,6 +2001,84 @@
-0.05233597010374069
]
},
+ {
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+ "id": 248,
+ "link": "Ellbow",
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+ {
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+ ]
+ },
{
"name": "Arm2_marker_120",
"id": 120,
diff --git a/data/simulation/Scene9a/render_a.npz b/data/simulation/Scene9a/render_a.npz
index cdb93c1..5a60970 100644
Binary files a/data/simulation/Scene9a/render_a.npz and b/data/simulation/Scene9a/render_a.npz differ
diff --git a/data/simulation/Scene9a/render_a.png b/data/simulation/Scene9a/render_a.png
index f299dae..9b70d54 100644
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diff --git a/data/simulation/Scene9a/render_b.json b/data/simulation/Scene9a/render_b.json
index 6870bb5..e812ff0 100644
--- a/data/simulation/Scene9a/render_b.json
+++ b/data/simulation/Scene9a/render_b.json
@@ -2001,6 +2001,84 @@
-0.05233597010374069
]
},
+ {
+ "name": "aruco_248",
+ "id": 248,
+ "link": "Ellbow",
+ "position_m": [
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+ -0.17729905247688293,
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+ ]
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+ "link": "Ellbow",
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+ {
+ "name": "aruco_231",
+ "id": 231,
+ "link": "Ellbow",
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+ -0.7431447505950928
+ ]
+ },
{
"name": "Arm2_marker_120",
"id": 120,
diff --git a/data/simulation/Scene9a/render_b.npz b/data/simulation/Scene9a/render_b.npz
index cdb93c1..5a60970 100644
Binary files a/data/simulation/Scene9a/render_b.npz and b/data/simulation/Scene9a/render_b.npz differ
diff --git a/data/simulation/Scene9a/render_b.png b/data/simulation/Scene9a/render_b.png
index 4348629..98dc91d 100644
Binary files a/data/simulation/Scene9a/render_b.png and b/data/simulation/Scene9a/render_b.png differ
diff --git a/data/simulation/Scene9a/render_c.json b/data/simulation/Scene9a/render_c.json
index 6870bb5..e812ff0 100644
--- a/data/simulation/Scene9a/render_c.json
+++ b/data/simulation/Scene9a/render_c.json
@@ -2001,6 +2001,84 @@
-0.05233597010374069
]
},
+ {
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+ "id": 248,
+ "link": "Ellbow",
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+ -0.9986295700073242,
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+ ]
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+ {
+ "name": "aruco_232",
+ "id": 232,
+ "link": "Ellbow",
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+ "rotation_quaternion": [
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+ ],
+ "normal": [
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+ -0.7431448698043823,
+ -0.6691306233406067
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+ },
+ {
+ "name": "aruco_231",
+ "id": 231,
+ "link": "Ellbow",
+ "position_m": [
+ 0.25999999046325684,
+ -0.1180923730134964,
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+ "position_mm": [
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+ ]
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{
"name": "Arm2_marker_120",
"id": 120,
diff --git a/data/simulation/Scene9a/render_c.npz b/data/simulation/Scene9a/render_c.npz
index cdb93c1..5a60970 100644
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diff --git a/data/simulation/Scene9a/render_c.png b/data/simulation/Scene9a/render_c.png
index 0f46435..abf83dc 100644
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diff --git a/data/simulation/Scene9a/render_d.json b/data/simulation/Scene9a/render_d.json
index 6870bb5..e812ff0 100644
--- a/data/simulation/Scene9a/render_d.json
+++ b/data/simulation/Scene9a/render_d.json
@@ -2001,6 +2001,84 @@
-0.05233597010374069
]
},
+ {
+ "name": "aruco_248",
+ "id": 248,
+ "link": "Ellbow",
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+ -0.17729905247688293,
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+ ]
+ },
+ {
+ "name": "aruco_232",
+ "id": 232,
+ "link": "Ellbow",
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{
"name": "Arm2_marker_120",
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diff --git a/data/simulation/Scene9a/render_d.npz b/data/simulation/Scene9a/render_d.npz
index cdb93c1..5a60970 100644
Binary files a/data/simulation/Scene9a/render_d.npz and b/data/simulation/Scene9a/render_d.npz differ
diff --git a/data/simulation/Scene9a/render_d.png b/data/simulation/Scene9a/render_d.png
index db972b2..01c8617 100644
Binary files a/data/simulation/Scene9a/render_d.png and b/data/simulation/Scene9a/render_d.png differ
diff --git a/data/simulation/Scene9a/render_e.json b/data/simulation/Scene9a/render_e.json
index 6870bb5..e812ff0 100644
--- a/data/simulation/Scene9a/render_e.json
+++ b/data/simulation/Scene9a/render_e.json
@@ -2001,6 +2001,84 @@
-0.05233597010374069
]
},
+ {
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{
"name": "Arm2_marker_120",
"id": 120,
diff --git a/data/simulation/Scene9a/render_e.npz b/data/simulation/Scene9a/render_e.npz
index c76f892..5a60970 100644
Binary files a/data/simulation/Scene9a/render_e.npz and b/data/simulation/Scene9a/render_e.npz differ
diff --git a/data/simulation/Scene9a/render_e.png b/data/simulation/Scene9a/render_e.png
index 0aa576a..83a6f25 100644
Binary files a/data/simulation/Scene9a/render_e.png and b/data/simulation/Scene9a/render_e.png differ
diff --git a/data/simulation/Scene9a/render_f.json b/data/simulation/Scene9a/render_f.json
index 6870bb5..e812ff0 100644
--- a/data/simulation/Scene9a/render_f.json
+++ b/data/simulation/Scene9a/render_f.json
@@ -2001,6 +2001,84 @@
-0.05233597010374069
]
},
+ {
+ "name": "aruco_248",
+ "id": 248,
+ "link": "Ellbow",
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+ {
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+ ]
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{
"name": "Arm2_marker_120",
"id": 120,
diff --git a/data/simulation/Scene9a/render_f.npz b/data/simulation/Scene9a/render_f.npz
index cdb93c1..5a60970 100644
Binary files a/data/simulation/Scene9a/render_f.npz and b/data/simulation/Scene9a/render_f.npz differ
diff --git a/data/simulation/Scene9a/render_f.png b/data/simulation/Scene9a/render_f.png
index f667a6e..e30b1a9 100644
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diff --git a/data/simulation/Scene9a/render_g.json b/data/simulation/Scene9a/render_g.json
index 6870bb5..e812ff0 100644
--- a/data/simulation/Scene9a/render_g.json
+++ b/data/simulation/Scene9a/render_g.json
@@ -2001,6 +2001,84 @@
-0.05233597010374069
]
},
+ {
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+ "id": 248,
+ "link": "Ellbow",
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+ {
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+ "link": "Ellbow",
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{
"name": "Arm2_marker_120",
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diff --git a/data/simulation/Scene9a/render_g.npz b/data/simulation/Scene9a/render_g.npz
index cdb93c1..5a60970 100644
Binary files a/data/simulation/Scene9a/render_g.npz and b/data/simulation/Scene9a/render_g.npz differ
diff --git a/data/simulation/Scene9a/render_g.png b/data/simulation/Scene9a/render_g.png
index 6d3b006..f6d6349 100644
Binary files a/data/simulation/Scene9a/render_g.png and b/data/simulation/Scene9a/render_g.png differ
diff --git a/data/simulation/Scene9b/pose.json b/data/simulation/Scene9b/pose.json
index 89d6a32..bb6a645 100644
--- a/data/simulation/Scene9b/pose.json
+++ b/data/simulation/Scene9b/pose.json
@@ -9,6 +9,11 @@
"c": 9,
"e": 8
},
+ "rendering": {
+ "width": 4896,
+ "height": 3264,
+ "dofFStop": 5.6
+ },
"camera_positions": {
"a": [
-300,
@@ -45,5 +50,42 @@
200,
1400
]
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+ "camera_targets": {
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+ 180
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+ "g": [
+ 200,
+ -200,
+ 180
+ ]
}
}
\ No newline at end of file
diff --git a/data/simulation/debug/markers.json b/data/simulation/debug/markers.json
index a7fb394..e812ff0 100644
--- a/data/simulation/debug/markers.json
+++ b/data/simulation/debug/markers.json
@@ -1798,12 +1798,12 @@
"id": 198,
"link": "Arm1",
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@@ -1824,12 +1824,12 @@
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],
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@@ -1850,12 +1850,12 @@
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],
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@@ -1876,12 +1876,12 @@
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@@ -1902,12 +1902,12 @@
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],
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@@ -1928,12 +1928,12 @@
"id": 245,
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],
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@@ -1954,12 +1954,12 @@
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],
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@@ -1980,12 +1980,12 @@
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],
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],
@@ -2006,12 +2006,12 @@
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],
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],
@@ -2032,12 +2032,12 @@
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],
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],
@@ -2058,12 +2058,12 @@
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@@ -2084,25 +2084,25 @@
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{
@@ -2110,25 +2110,25 @@
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@@ -2136,25 +2136,25 @@
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{
@@ -2162,25 +2162,25 @@
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{
@@ -2188,25 +2188,25 @@
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@@ -2214,25 +2214,25 @@
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+ -100.19111633300781,
+ 232.34176635742188
],
"rotation_quaternion": [
- 0.14898721873760223,
- -0.15699973702430725,
- -0.6720378994941711,
- 0.7081800699234009
+ 0.6720378398895264,
+ -0.7081800699234009,
+ 0.148987278342247,
+ -0.15699981153011322
],
"normal": [
- -0.4226182699203491,
- -0.9050657749176025,
- 0.047432348132133484
+ 0.42261838912963867,
+ 0.9050655961036682,
+ -0.047432444989681244
]
},
{
@@ -2240,25 +2240,25 @@
"id": 124,
"link": "Arm2",
"position_m": [
- 0.19664090871810913,
- -0.14251121878623962,
- 0.27161043882369995
+ 0.29335907101631165,
+ -0.11826106905937195,
+ 0.2703395485877991
],
"position_mm": [
- 196.64090871810913,
- -142.51121878623962,
- 271.61043882369995
+ 293.35907101631165,
+ -118.26106905937195,
+ 270.3395485877991
],
"rotation_quaternion": [
- 0.3948240280151367,
- -0.41605767607688904,
- -0.5638671517372131,
- 0.59419184923172
+ 0.5638670921325684,
+ -0.5941917896270752,
+ 0.3948240578174591,
+ -0.4160577356815338
],
"normal": [
- -0.9396926164627075,
- -0.34155139327049255,
- 0.017899779602885246
+ 0.9396927356719971,
+ 0.34155115485191345,
+ -0.017899811267852783
]
},
{
@@ -2266,25 +2266,25 @@
"id": 219,
"link": "Arm2",
"position_m": [
- 0.26335909962654114,
- -0.11826106160879135,
- 0.2703395485877991
+ 0.22664089500904083,
+ -0.14251121878623962,
+ 0.27161043882369995
],
"position_mm": [
- 263.35909962654114,
- -118.26106160879135,
- 270.3395485877991
+ 226.64089500904083,
+ -142.51121878623962,
+ 271.61043882369995
],
"rotation_quaternion": [
- 0.5638671517372131,
- -0.5941919088363647,
- 0.39482399821281433,
- -0.41605764627456665
+ 0.3948240280151367,
+ -0.4160577058792114,
+ -0.5638670921325684,
+ 0.59419184923172
],
"normal": [
- 0.9396926164627075,
- 0.3415515422821045,
- -0.017899898812174797
+ -0.9396926760673523,
+ -0.3415512442588806,
+ 0.017899809405207634
]
},
{
@@ -2292,25 +2292,25 @@
"id": 40,
"link": "FingerA",
"position_m": [
- 0.2677094340324402,
- -0.1501556783914566,
- 0.35217177867889404
+ 0.21553662419319153,
+ -0.08246307820081711,
+ 0.31723877787590027
],
"position_mm": [
- 267.7094340324402,
- -150.1556783914566,
- 352.17177867889404
+ 215.53662419319153,
+ -82.46307820081711,
+ 317.23877787590027
],
"rotation_quaternion": [
- 0.021059956401586533,
- 0.06727804243564606,
- -0.8116653561592102,
- 0.5798529982566833
+ 0.25081899762153625,
+ -0.9551512002944946,
+ -0.13290834426879883,
+ -0.08432948589324951
],
"normal": [
- 0.04383547604084015,
- -0.9441269040107727,
- -0.32665398716926575
+ 0.09442292153835297,
+ 0.5015562772750854,
+ -0.859956681728363
]
},
{
@@ -2318,25 +2318,25 @@
"id": 41,
"link": "FingerA",
"position_m": [
- 0.2358827143907547,
- -0.11256645619869232,
- 0.34673067927360535
+ 0.24487937986850739,
+ -0.11210105568170547,
+ 0.34399858117103577
],
"position_mm": [
- 235.8827143907547,
- -112.56645619869232,
- 346.73067927360535
+ 244.87937986850739,
+ -112.10105568170547,
+ 343.99858117103577
],
"rotation_quaternion": [
- 0.9692426919937134,
- 0.002282005036249757,
- -0.018137134611606598,
- 0.24542734026908875
+ 0.22204725444316864,
+ 0.003033978631719947,
+ -0.3654671907424927,
+ -0.9039466977119446
],
"normal": [
- -0.03403843566775322,
- -0.013326330110430717,
- 0.9993317127227783
+ -0.16778706014156342,
+ 0.6593782901763916,
+ 0.73284912109375
]
},
{
@@ -2344,25 +2344,25 @@
"id": 42,
"link": "FingerA",
"position_m": [
- 0.2639923095703125,
- -0.13954909145832062,
- 0.3735145628452301
+ 0.21405166387557983,
+ -0.0764177069067955,
+ 0.34054282307624817
],
"position_mm": [
- 263.9923095703125,
- -139.54909145832062,
- 373.5145628452301
+ 214.05166387557983,
+ -76.4177069067955,
+ 340.54282307624817
],
"rotation_quaternion": [
- 0.8447493314743042,
- -0.3168453276157379,
- 0.42179709672927856,
- 0.0899711325764656
+ 0.2647150754928589,
+ 0.453290730714798,
+ -0.008107217960059643,
+ -0.8511097431182861
],
"normal": [
- 0.6556118130683899,
- 0.6112089157104492,
- 0.4433925151824951
+ -0.7758923768997192,
+ -0.22618548572063446,
+ 0.5889236330986023
]
},
{
@@ -2370,25 +2370,25 @@
"id": 43,
"link": "FingerB",
"position_m": [
- 0.2080598622560501,
- -0.1449533998966217,
- 0.36243146657943726
+ 0.26951199769973755,
+ -0.07608000934123993,
+ 0.34051966667175293
],
"position_mm": [
- 208.0598622560501,
- -144.9533998966217,
- 362.43146657943726
+ 269.51199769973755,
+ -76.08000934123993,
+ 340.51966667175293
],
"rotation_quaternion": [
- 0.526361346244812,
- -0.39512625336647034,
- 0.42490965127944946,
- 0.6215068101882935
+ 0.7693742513656616,
+ 0.23698587715625763,
+ 0.11772578954696655,
+ -0.5814135074615479
],
"normal": [
- -0.043835271149873734,
- 0.9441269636154175,
- 0.3266540467739105
+ -0.09442318975925446,
+ -0.5015563368797302,
+ 0.859956681728363
]
},
{
@@ -2396,25 +2396,25 @@
"id": 44,
"link": "FingerB",
"position_m": [
- 0.23418523371219635,
- -0.16895513236522675,
- 0.3278355598449707
+ 0.25280579924583435,
+ -0.08222946524620056,
+ 0.2942748963832855
],
"position_mm": [
- 234.18523371219635,
- -168.95513236522675,
- 327.8355598449707
+ 252.80579924583435,
+ -82.22946524620056,
+ 294.2748963832855
],
"rotation_quaternion": [
- 0.47481709718704224,
- 0.28831005096435547,
- -0.19160225987434387,
- 0.8091443777084351
+ 0.6558026671409607,
+ -0.4080226421356201,
+ -0.521297037601471,
+ -0.3628908097743988
],
"normal": [
- 0.2846168577671051,
- -0.5838568210601807,
- 0.7603318095207214
+ -0.38760069012641907,
+ 0.9135125875473022,
+ 0.12353380024433136
]
},
{
@@ -2422,25 +2422,25 @@
"id": 45,
"link": "FingerB",
"position_m": [
- 0.21609467267990112,
- -0.16585004329681396,
- 0.37140941619873047
+ 0.2614270746707916,
+ -0.055022913962602615,
+ 0.3319719433784485
],
"position_mm": [
- 216.09467267990112,
- -165.85004329681396,
- 371.40941619873047
+ 261.4270746707916,
+ -55.022913962602615,
+ 331.9719433784485
],
"rotation_quaternion": [
- 0.6045908331871033,
- 0.20278345048427582,
- -0.6249297261238098,
- 0.45034608244895935
+ 0.18035854399204254,
+ -0.6729050278663635,
+ -0.44777974486351013,
+ -0.5605023503303528
],
"normal": [
- -0.573008120059967,
- -0.808071494102478,
- 0.136683389544487
+ 0.592807948589325,
+ 0.7446916103363037,
+ -0.30661579966545105
]
}
]
\ No newline at end of file
diff --git a/data/simulation/debug/render.npz b/data/simulation/debug/render.npz
index b10ee26..c76f892 100644
Binary files a/data/simulation/debug/render.npz and b/data/simulation/debug/render.npz differ
diff --git a/data/simulation/debug/render.png b/data/simulation/debug/render.png
index 235204b..f6d6349 100644
Binary files a/data/simulation/debug/render.png and b/data/simulation/debug/render.png differ
diff --git a/pipeline/3_anchor_patch.py b/pipeline/3_anchor_patch.py
deleted file mode 100644
index e69de29..0000000
diff --git a/pipeline/3_multiview_bundle_adjustment_v1.py b/pipeline/3_multiview_bundle_adjustment_v1.py
deleted file mode 100644
index 705f90f..0000000
--- a/pipeline/3_multiview_bundle_adjustment_v1.py
+++ /dev/null
@@ -1,674 +0,0 @@
-#!/usr/bin/env python3
-"""
-3_multiview_bundle_adjustment.py
-
-Multi-view ArUco marker position optimization with geometric constraints.
-
-Strategy:
-1. Triangulate markers from multi-view observations (rough initial estimate)
-2. Optimize marker positions with constraints:
- - Same-link distance constraints: markers on same link keep fixed relative distance
- - Joint-axis constraints: markers on linked arms via joint keep distance along joint axis
-3. Use bundle adjustment: minimize reprojection error + constraint violations
-
-Key insight: We do NOT use robot.json marker positions (they're in different joint config).
-We only use robot.json hierarchy to DEFINE constraints.
-
-Dependencies: numpy, opencv-python, scipy
-"""
-
-from __future__ import annotations
-
-import argparse
-import json
-import os
-import sys
-import time
-from typing import Any, Dict, List, Optional, Tuple
-
-import cv2
-import numpy as np
-
-
-# ===================================================================
-# Path / JSON helpers
-# ===================================================================
-
-def resolve_path(path: str) -> str:
- path = os.path.expanduser(path)
- if os.path.isabs(path):
- return path
- return os.path.abspath(path)
-
-
-def load_json(path: str) -> Dict[str, Any]:
- with open(resolve_path(path), "r", encoding="utf-8") as f:
- return json.load(f)
-
-
-def save_json(path: str, data: Dict[str, Any]) -> None:
- with open(resolve_path(path), "w", encoding="utf-8") as f:
- json.dump(data, f, indent=2)
-
-
-# ===================================================================
-# Units
-# ===================================================================
-
-def get_length_scale(robot_data: Dict[str, Any]) -> float:
- units = robot_data.get("units", {})
- length_unit = str(units.get("length", "")).strip().lower()
- if length_unit in ("mm", "millimeter", "millimeters"):
- return 1.0 / 1000.0
- elif length_unit in ("cm", "centimeter", "centimeters"):
- return 1.0 / 100.0
- return 1.0
-
-
-# ===================================================================
-# Constraint definitions (from robot.json structure ONLY)
-# ===================================================================
-
-class MarkerDistanceConstraint:
- """
- Two markers on the same link must maintain a fixed distance.
- We compute this distance from their relative positions in robot.json,
- but scaled to meters. The ACTUAL 3D positions will be optimized,
- but their distance must stay constant.
- """
- def __init__(self, marker_id_a: int, marker_id_b: int,
- link_name: str, target_distance: float, weight: float = 1.0):
- self.marker_id_a = marker_id_a
- self.marker_id_b = marker_id_b
- self.link_name = link_name
- self.target_distance = target_distance
- self.weight = weight
-
-
-class JointAxisConstraint:
- """
- Two markers on linked arms (connected by joint around axis).
- If joint axis is [1,0,0], then delta_x component must stay constant.
- """
- def __init__(self, marker_id_parent: int, marker_id_child: int,
- parent_link: str, child_link: str,
- joint_axis: np.ndarray, # unit vector [x,y,z]
- target_delta_along_axis: float,
- weight: float = 1.0):
- self.marker_id_parent = marker_id_parent
- self.marker_id_child = marker_id_child
- self.parent_link = parent_link
- self.child_link = child_link
- self.joint_axis = joint_axis / (np.linalg.norm(joint_axis) + 1e-9)
- self.target_delta_along_axis = target_delta_along_axis
- self.weight = weight
-
-
-def extract_constraints_from_hierarchy(robot_data: Dict[str, Any],
- length_scale: float) -> Tuple[Dict, List]:
- """
- Extract constraint definitions from robot.json.
-
- NOTE: We only extract CONSTRAINT TYPES, not actual marker positions.
- Marker positions will come from triangulation.
-
- Returns:
- marker_to_link: {marker_id -> link_name}
- constraints: List of constraint objects
- """
- constraints = []
- marker_to_link = {}
-
- links = robot_data.get("links", {})
-
- # Build map: link_name -> markers on that link
- link_markers = {}
- for link_name, link_data in links.items():
- markers = link_data.get("markers", [])
- marker_ids = []
- marker_positions = {}
-
- for marker in markers:
- marker_id = int(marker.get("id", -1))
- pos = marker.get("position", None)
-
- if marker_id >= 0 and pos is not None and len(pos) == 3:
- marker_ids.append(marker_id)
- marker_positions[marker_id] = np.array(pos, dtype=np.float32) * np.float32(length_scale)
- marker_to_link[marker_id] = link_name
-
- link_markers[link_name] = (marker_ids, marker_positions)
-
- # Extract same-link distance constraints
- for link_name, (marker_ids, positions) in link_markers.items():
- if len(marker_ids) >= 2:
- # For each pair, compute their distance
- for i in range(len(marker_ids)):
- for j in range(i + 1, len(marker_ids)):
- mid_a = marker_ids[i]
- mid_b = marker_ids[j]
- pos_a = positions[mid_a]
- pos_b = positions[mid_b]
- target_dist = float(np.linalg.norm(pos_b - pos_a))
-
- constraint = MarkerDistanceConstraint(
- mid_a, mid_b, link_name, target_dist, weight=1.0
- )
- constraints.append(constraint)
-
- # Extract joint-axis constraints (if we have markers on parent and child links)
- for link_name, link_data in links.items():
- parent_link = link_data.get("parent", None)
-
- if parent_link is None:
- continue
-
- joint_info = link_data.get("jointToParent", {})
- if not joint_info:
- continue
-
- joint_axis = joint_info.get("axis", [1, 0, 0])
- joint_axis = np.array(joint_axis, dtype=np.float32)
-
- # Get markers on this link and parent link
- child_marker_ids, child_positions = link_markers.get(link_name, ([], {}))
- parent_marker_ids, parent_positions = link_markers.get(parent_link, ([], {}))
-
- if len(child_marker_ids) > 0 and len(parent_marker_ids) > 0:
- # Create constraint between first marker of parent and first marker of child
- for mid_parent in parent_marker_ids:
- for mid_child in child_marker_ids:
-
- if mid_parent == mid_child:
- continue
-
- pos_parent = parent_positions[mid_parent]
- pos_child = child_positions[mid_child]
-
- delta = pos_child - pos_parent
-
- target_delta_along_axis = float(
- np.dot(delta, joint_axis)
- )
-
- constraint = JointAxisConstraint(
- mid_parent,
- mid_child,
- parent_link,
- link_name,
- joint_axis,
- target_delta_along_axis,
- weight=0.5
- )
-
- constraints.append(constraint)
-
- return marker_to_link, constraints
-
-# ===
-# Helper Function
-#
-
-def print_constraints_with_errors(
- title: str,
- constraints: List,
- positions: Dict[int, np.ndarray]
-) -> None:
-
- print(f"\n[INFO] {title}")
-
- for idx, constraint in enumerate(constraints):
-
- # ----------------------------------------------------------
- # Distance constraint
- # ----------------------------------------------------------
-
- if isinstance(constraint, MarkerDistanceConstraint):
-
- if (
- constraint.marker_id_a not in positions or
- constraint.marker_id_b not in positions
- ):
- continue
-
- pos_a = positions[constraint.marker_id_a]
- pos_b = positions[constraint.marker_id_b]
-
- actual_dist = np.linalg.norm(pos_b - pos_a)
-
- error_m = actual_dist - constraint.target_distance
-
- print(
- f" [{idx:03d}] DISTANCE | "
- f"Link='{constraint.link_name}' | "
- f"M{constraint.marker_id_a} <-> M{constraint.marker_id_b} | "
- f"target={constraint.target_distance*1000:.2f} mm | "
- f"actual={actual_dist*1000:.2f} mm | "
- f"error={error_m*1000:+.2f} mm"
- )
-
- # ----------------------------------------------------------
- # Joint-axis constraint
- # ----------------------------------------------------------
-
- elif isinstance(constraint, JointAxisConstraint):
-
- if (
- constraint.marker_id_parent not in positions or
- constraint.marker_id_child not in positions
- ):
- continue
-
- pos_parent = positions[constraint.marker_id_parent]
- pos_child = positions[constraint.marker_id_child]
-
- delta = pos_child - pos_parent
-
- actual = np.dot(delta, constraint.joint_axis)
-
- error_m = actual - constraint.target_delta_along_axis
-
- axis_str = np.array2string(
- constraint.joint_axis,
- precision=2,
- suppress_small=True
- )
-
- print(
- f" [{idx:03d}] JOINT_AXIS | "
- f"{constraint.parent_link}(M{constraint.marker_id_parent}) -> "
- f"{constraint.child_link}(M{constraint.marker_id_child}) | "
- f"axis={axis_str} | "
- f"target={constraint.target_delta_along_axis*1000:.2f} mm | "
- f"actual={actual*1000:.2f} mm | "
- f"error={error_m*1000:+.2f} mm"
- )
-
-# ===================================================================
-# Multi-view loading
-# ===================================================================
-
-def load_observations_and_poses(
- detection_files: List[str],
- pose_files: List[str]
-) -> Tuple[Dict, List, List]:
- """Load observations and camera poses."""
- if len(detection_files) != len(pose_files):
- raise ValueError(f"Mismatch: {len(detection_files)} detections vs {len(pose_files)} poses")
-
- marker_observations = {}
- cameras = []
- obs_metadata = []
-
- for cam_idx, (det_file, pose_file) in enumerate(zip(detection_files, pose_files)):
- det = load_json(det_file)
- pose_data = load_json(pose_file)
-
- # Camera intrinsics
- cam_section = det.get("camera", {})
- K = np.array(cam_section.get("camera_matrix", []), dtype=np.float32).reshape(3, 3)
- D = np.array(cam_section.get("distortion_coefficients", []), dtype=np.float32).reshape(-1, 1)
-
- # Camera pose
- pose_section = pose_data.get("camera_pose", {})
- world_to_cam = pose_section.get("world_to_camera", {})
- R_wc = np.array(world_to_cam.get("rotation_matrix", []), dtype=np.float32).reshape(3, 3)
- t_wc = np.array(world_to_cam.get("translation_m", []), dtype=np.float32).reshape(3)
-
- cameras.append((K, D, R_wc, t_wc))
-
- # Extract observations
- detections = det.get("detections", [])
- for det_obj in detections:
- marker_id = int(det_obj.get("marker_id", -1))
- if marker_id < 0:
- continue
-
- center_px = np.array(det_obj.get("center_px", []), dtype=np.float32)
-
- # Undistort to normalized coordinates
- pts = center_px.reshape(1, 1, 2).astype(np.float32)
- und = cv2.undistortPoints(pts, K, D, P=None)
- norm_coords = und.reshape(2).astype(np.float32)
-
- if marker_id not in marker_observations:
- marker_observations[marker_id] = []
-
- marker_observations[marker_id].append((cam_idx, norm_coords))
-
- return marker_observations, cameras, obs_metadata
-
-
-# ===================================================================
-# Initial triangulation
-# ===================================================================
-
-def triangulate_marker_initial(
- marker_id: int,
- observations: List[Tuple[int, np.ndarray]],
- cameras: List[Tuple]
-) -> Optional[np.ndarray]:
- """Rough triangulation using first two views."""
- if len(observations) < 2:
- return None
-
- K1, D1, R_wc_1, t_wc_1 = cameras[observations[0][0]]
- K2, D2, R_wc_2, t_wc_2 = cameras[observations[1][0]]
-
- norm_coords_1 = observations[0][1]
- norm_coords_2 = observations[1][1]
-
- # Convert to pixel coordinates
- x1_px = K1[0, 0] * norm_coords_1[0] + K1[0, 2]
- y1_px = K1[1, 1] * norm_coords_1[1] + K1[1, 2]
-
- x2_px = K2[0, 0] * norm_coords_2[0] + K2[0, 2]
- y2_px = K2[1, 1] * norm_coords_2[1] + K2[1, 2]
-
- P1 = K1 @ np.hstack([R_wc_1, t_wc_1.reshape(3, 1)])
- P2 = K2 @ np.hstack([R_wc_2, t_wc_2.reshape(3, 1)])
-
- try:
- X_h = cv2.triangulatePoints(P1, P2,
- np.array([[x1_px], [y1_px]]).astype(np.float32),
- np.array([[x2_px], [y2_px]]).astype(np.float32))
- X = (X_h[:3] / X_h[3]).reshape(3).astype(np.float32)
- return X
- except Exception:
- return None
-
-
-def initial_triangulation(
- marker_observations: Dict[int, List[Tuple[int, np.ndarray]]],
- cameras: List[Tuple]
-) -> Dict[int, np.ndarray]:
- """Compute initial marker positions via triangulation."""
- triangulated = {}
-
- for marker_id, observations in marker_observations.items():
- X = triangulate_marker_initial(marker_id, observations, cameras)
- if X is not None:
- triangulated[marker_id] = X
-
- return triangulated
-
-
-# ===================================================================
-# Bundle adjustment with constraints
-# ===================================================================
-
-def bundle_adjustment_residuals(
- marker_positions_flat: np.ndarray,
- marker_ids: List[int],
- marker_observations: Dict[int, List[Tuple[int, np.ndarray]]],
- cameras: List[Tuple],
- constraints: List,
- lambda_constraint: float = 100.0
-) -> np.ndarray:
- """
- Compute residuals for bundle adjustment.
-
- marker_positions_flat: [x0, y0, z0, x1, y1, z1, ...]
-
- Returns: [reprojection_residuals, constraint_residuals]
- """
- marker_dict = {}
- for i, marker_id in enumerate(marker_ids):
- marker_dict[marker_id] = marker_positions_flat[i*3:(i+1)*3]
-
- residuals = []
-
- # Reprojection residuals
- for marker_id, observations in marker_observations.items():
- if marker_id not in marker_dict:
- continue
-
- X_world = marker_dict[marker_id]
-
- for cam_idx, norm_coords_obs in observations:
- K, D, R_wc, t_wc = cameras[cam_idx]
-
- # Project
- X_cam = R_wc @ X_world + t_wc
- if X_cam[2] > 0.001: # in front of camera
- proj_norm = X_cam[:2] / X_cam[2]
- residual = proj_norm - norm_coords_obs
- residuals.append(residual[0])
- residuals.append(residual[1])
-
- # Constraint residuals
- for constraint in constraints:
- if isinstance(constraint, MarkerDistanceConstraint):
- if constraint.marker_id_a in marker_dict and constraint.marker_id_b in marker_dict:
- pos_a = marker_dict[constraint.marker_id_a]
- pos_b = marker_dict[constraint.marker_id_b]
- actual_dist = np.linalg.norm(pos_b - pos_a)
- residual = (actual_dist - constraint.target_distance) * constraint.weight * lambda_constraint
- residuals.append(residual)
-
- elif isinstance(constraint, JointAxisConstraint):
- if constraint.marker_id_parent in marker_dict and constraint.marker_id_child in marker_dict:
- pos_parent = marker_dict[constraint.marker_id_parent]
- pos_child = marker_dict[constraint.marker_id_child]
- delta = pos_child - pos_parent
- actual_delta_along_axis = np.dot(delta, constraint.joint_axis)
- residual = (actual_delta_along_axis - constraint.target_delta_along_axis) * constraint.weight * lambda_constraint
- residuals.append(residual)
-
- return np.array(residuals, dtype=np.float64)
-
-
-def optimize_with_constraints(
- initial_positions: Dict[int, np.ndarray],
- marker_observations: Dict[int, List[Tuple[int, np.ndarray]]],
- cameras: List[Tuple],
- constraints: List,
- lambda_constraint: float = 100.0,
- max_iterations: int = 50
-) -> Dict[int, np.ndarray]:
- """
- Optimize marker positions using scipy least squares.
- """
- try:
- from scipy.optimize import least_squares
- except ImportError:
- print("[WARN] scipy not available, skipping optimization. Using initial triangulation.")
- return initial_positions
-
- marker_ids = sorted(initial_positions.keys())
- x0 = np.concatenate([initial_positions[mid] for mid in marker_ids])
-
- def residuals_fn(x):
- return bundle_adjustment_residuals(x, marker_ids, marker_observations, cameras,
- constraints, lambda_constraint)
-
- print(f"[INFO] Starting optimization with {len(x0)} variables and {len(constraints)} constraints...")
-
- result = least_squares(
- residuals_fn,
- x0,
- max_nfev=max_iterations * len(marker_ids),
- verbose=1
- )
-
- optimized_flat = result.x
- optimized = {}
-
- for i, marker_id in enumerate(marker_ids):
- optimized[marker_id] = optimized_flat[i*3:(i+1)*3]
-
- print(f"[INFO] Optimization complete. Final cost: {np.sum(result.fun**2):.6f}")
-
- return optimized
-
-
-# ===================================================================
-# Main
-# ===================================================================
-
-def main() -> None:
- parser = argparse.ArgumentParser(
- description="Multi-view bundle adjustment with geometric constraints"
- )
- parser.add_argument(
- "-det", "--detections",
- action="append",
- required=True,
- help="*_aruco_detection.json files"
- )
- parser.add_argument(
- "-pose", "--poses",
- action="append",
- required=True,
- help="*_camera_pose.json files"
- )
- parser.add_argument(
- "-robot", "--robot",
- required=True,
- help="robot.json"
- )
- parser.add_argument(
- "-outDir", "--outDir",
- default=None,
- help="Output directory"
- )
- parser.add_argument(
- "-lambdaWeight", "--lambdaWeight",
- type=float,
- default=100.0,
- help="Constraint weight"
- )
-
- args = parser.parse_args()
-
- if len(args.detections) != len(args.poses):
- print(f"[ERROR] Mismatch: {len(args.detections)} vs {len(args.poses)}")
- sys.exit(1)
-
- # Load robot
- robot_data = load_json(args.robot)
- length_scale = get_length_scale(robot_data)
-
- print("[STEP 1] Extract constraint definitions from robot.json hierarchy...")
- marker_to_link, constraints = extract_constraints_from_hierarchy(robot_data, length_scale)
- print(f"[INFO] Extracted {len(constraints)} constraints")
-
- print("\n[INFO] Constraint list:")
-
- for idx, constraint in enumerate(constraints):
-
- if isinstance(constraint, MarkerDistanceConstraint):
- print(
- f" [{idx:03d}] DISTANCE | "
- f"Link='{constraint.link_name}' | "
- f"Marker {constraint.marker_id_a} <-> {constraint.marker_id_b} | "
- f"TargetDistance={constraint.target_distance:.6f} m | "
- f"Weight={constraint.weight}"
- )
-
- elif isinstance(constraint, JointAxisConstraint):
- axis_str = np.array2string(
- constraint.joint_axis,
- precision=3,
- suppress_small=True
- )
-
- print(
- f" [{idx:03d}] JOINT_AXIS | "
- f"{constraint.parent_link}({constraint.marker_id_parent}) -> "
- f"{constraint.child_link}({constraint.marker_id_child}) | "
- f"Axis={axis_str} | "
- f"TargetDelta={constraint.target_delta_along_axis:.6f} m | "
- f"Weight={constraint.weight}"
- )
-
- print("\n[STEP 2] Load observations and camera poses...")
- marker_observations, cameras, _ = load_observations_and_poses(args.detections, args.poses)
- print(f"[INFO] {len(cameras)} cameras, {len(marker_observations)} observed markers")
-
- print("\n[STEP 3] Initial triangulation...")
- initial_pos = initial_triangulation(marker_observations, cameras)
- print(f"[INFO] Triangulated {len(initial_pos)} markers")
-
- # ------------------------------------------------------------
- # Save initial triangulated positions (before optimization)
- # ------------------------------------------------------------
-
- initial_output_markers = []
-
- for marker_id, position in sorted(initial_pos.items()):
- initial_output_markers.append({
- "marker_id": int(marker_id),
- "position_m": [float(x) for x in position],
- "position_mm": [float(x * 1000.0) for x in position],
- "link": marker_to_link.get(marker_id, "unknown")
- })
-
- initial_output = {
- "schema_version": "1.0",
- "created_utc": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
- "summary": {
- "stage": "initial_triangulation",
- "num_cameras": len(cameras),
- "num_markers": len(initial_pos)
- },
- "markers": initial_output_markers
- }
-
- out_dir = args.outDir or os.path.dirname(args.detections[0]) or "."
- os.makedirs(resolve_path(out_dir), exist_ok=True)
-
- initial_out_file = os.path.join(
- out_dir,
- "aruco_positions_initial.json"
- )
-
- save_json(initial_out_file, initial_output)
-
- print(f"[INFO] Initial triangulation saved to {initial_out_file}")
-
-
- print("\n[STEP 4] Bundle adjustment with constraints...")
- optimized_pos = optimize_with_constraints(
- initial_pos, marker_observations, cameras, constraints,
- lambda_constraint=args.lambdaWeight
- )
-
- print_constraints_with_errors(
- "Constraint list AFTER optimization",
- constraints,
- optimized_pos
- )
-
-
- # Output
- output_markers = []
- for marker_id, position in sorted(optimized_pos.items()):
- output_markers.append({
- "marker_id": int(marker_id),
- "position_m": [float(x) for x in position],
- "position_mm": [float(x * 1000.0) for x in position],
- "link": marker_to_link.get(marker_id, "unknown")
- })
-
- output = {
- "schema_version": "1.0",
- "created_utc": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
- "summary": {
- "num_cameras": len(cameras),
- "num_markers": len(optimized_pos),
- "num_constraints": len(constraints)
- },
- "markers": output_markers
- }
-
- out_dir = args.outDir or os.path.dirname(args.detections[0]) or "."
- os.makedirs(resolve_path(out_dir), exist_ok=True)
- out_file = os.path.join(out_dir, "aruco_positions_optimized.json")
-
- save_json(out_file, output)
- print(f"\n[INFO] Saved to {out_file}")
-
-
-if __name__ == "__main__":
- main()
diff --git a/pipeline/3_multiview_bundle_adjustment_v2.py b/pipeline/3_multiview_bundle_adjustment_v2.py
deleted file mode 100644
index 615714d..0000000
--- a/pipeline/3_multiview_bundle_adjustment_v2.py
+++ /dev/null
@@ -1,1369 +0,0 @@
-#!/usr/bin/env python3
-"""
-3_multiview_bundle_adjustment_v2.py
-
-Multi-view ArUco marker position optimization with explicit, programmable
-constraint families.
-
-Main ideas:
-1) Triangulate initial marker positions from multi-view detections.
-2) Compile constraints from robot.json structure using rule-based recognition:
- - rigid link constraints (within a link)
- - joint-axis projection constraints (between directly connected links)
- - chain-axis projection constraints (ancestor/descendant propagation)
-3) Optimize marker positions with bundle adjustment:
- reprojection residuals + constraint residuals.
-
-This version is designed to be debuggable and switchable:
-- each constraint family can be enabled/disabled
-- robot.json may optionally include constraint_rules / constraint_overrides
-- duplicate marker IDs can be checked strictly or only warned about
-
-Dependencies:
- numpy, opencv-python, scipy (optional for optimization)
-
-Example:
- python 3_multiview_bundle_adjustment_v2.py ^
- -det cam1_aruco_detection.json cam2_aruco_detection.json cam3_aruco_detection.json ^
- -pose cam1_camera_pose.json cam2_camera_pose.json cam3_camera_pose.json ^
- -robot robot.json ^
- -lambdaWeight 100.0
-"""
-
-from __future__ import annotations
-
-import argparse
-import json
-import os
-import sys
-import time
-from dataclasses import dataclass
-from itertools import combinations
-from typing import Any, Dict, List, Optional, Tuple
-
-import cv2
-import numpy as np
-
-
-# ===================================================================
-# Path / JSON helpers
-# ===================================================================
-
-def resolve_path(path: str) -> str:
- path = os.path.expanduser(path)
- if os.path.isabs(path):
- return path
- return os.path.abspath(path)
-
-
-def load_json(path: str) -> Dict[str, Any]:
- with open(resolve_path(path), "r", encoding="utf-8") as f:
- return json.load(f)
-
-
-def save_json(path: str, data: Dict[str, Any]) -> None:
- with open(resolve_path(path), "w", encoding="utf-8") as f:
- json.dump(data, f, indent=2)
-
-
-# ===================================================================
-# Units
-# ===================================================================
-
-def get_length_scale(robot_data: Dict[str, Any]) -> float:
- units = robot_data.get("units", {})
- length_unit = str(units.get("length", "")).strip().lower()
- if length_unit in ("mm", "millimeter", "millimeters"):
- return 1.0 / 1000.0
- if length_unit in ("cm", "centimeter", "centimeters"):
- return 1.0 / 100.0
- return 1.0
-
-
-# ===================================================================
-# Small geometry helpers
-# ===================================================================
-
-def safe_norm(v: np.ndarray, eps: float = 1e-12) -> float:
- return float(np.linalg.norm(v) + eps)
-
-
-def normalize_vector(v: np.ndarray, eps: float = 1e-12) -> np.ndarray:
- n = safe_norm(v, eps)
- return np.asarray(v, dtype=np.float64) / n
-
-
-def principal_axis_id(axis: np.ndarray, threshold: float = 0.95) -> Optional[int]:
- """
- Return 0,1,2 for x,y,z if the axis is close enough to a principal axis.
- Return None if it is not sufficiently aligned.
- """
- a = normalize_vector(np.asarray(axis, dtype=np.float64))
- idx = int(np.argmax(np.abs(a)))
- if abs(a[idx]) >= threshold:
- return idx
- return None
-
-
-def axis_name(axis_id: int) -> str:
- return ["x", "y", "z"][axis_id]
-
-
-def camera_center_from_world_to_cam(R_wc: np.ndarray, t_wc: np.ndarray) -> np.ndarray:
- """
- world_to_camera: X_cam = R_wc * X_world + t_wc
- Camera center in world is -R^T t.
- """
- return -R_wc.T @ t_wc
-
-
-# ===================================================================
-# Constraint rules / config
-# ===================================================================
-
-@dataclass
-class ConstraintRuleConfig:
- # Rigid link constraints
- rigid_distance_enabled: bool = True
- rigid_distance_mode: str = "mst" # mst | star | full
- rigid_distance_weight: float = 1.0
-
- # Direct joint constraints (parent <-> child)
- joint_axis_enabled: bool = True
- joint_axis_max_pairs: int = 2 # number of anchor markers selected per side
- joint_axis_weight: float = 0.5
-
- # Propagated ancestor-descendant constraints
- chain_axis_enabled: bool = True
- chain_axis_max_depth: int = 3
- chain_axis_max_pairs: int = 2
- chain_axis_weight: float = 0.3
-
- # Axis recognition threshold
- axis_alignment_threshold: float = 0.95
-
- # If True, link-marker duplicates in robot.json are fatal.
- strict_unique_marker_ids: bool = False
-
- # Print/skipped diagnostics
- show_skipped_constraints: bool = True
-
-
-def _bool_or_default(value: Any, default: bool) -> bool:
- if value is None:
- return default
- return bool(value)
-
-
-def _float_or_default(value: Any, default: float) -> float:
- if value is None:
- return default
- return float(value)
-
-
-def _int_or_default(value: Any, default: int) -> int:
- if value is None:
- return default
- return int(value)
-
-
-def load_constraint_rule_config(robot_data: Dict[str, Any], args: argparse.Namespace) -> ConstraintRuleConfig:
- """
- Merge built-in defaults, optional robot.json constraint_rules, and CLI flags.
- """
- cfg = ConstraintRuleConfig()
-
- rules = robot_data.get("constraint_rules", {}) or {}
-
- rigid = rules.get("rigid_distance", {}) or {}
- joint = rules.get("joint_axis_projection", {}) or {}
- chain = rules.get("chain_axis_projection", {}) or {}
-
- cfg.rigid_distance_enabled = _bool_or_default(rigid.get("enabled"), cfg.rigid_distance_enabled)
- cfg.rigid_distance_mode = str(rigid.get("mode", cfg.rigid_distance_mode)).strip().lower()
- cfg.rigid_distance_weight = _float_or_default(rigid.get("weight"), cfg.rigid_distance_weight)
-
- cfg.joint_axis_enabled = _bool_or_default(joint.get("enabled"), cfg.joint_axis_enabled)
- cfg.joint_axis_max_pairs = _int_or_default(joint.get("max_pairs"), cfg.joint_axis_max_pairs)
- cfg.joint_axis_weight = _float_or_default(joint.get("weight"), cfg.joint_axis_weight)
-
- cfg.chain_axis_enabled = _bool_or_default(chain.get("enabled"), cfg.chain_axis_enabled)
- cfg.chain_axis_max_depth = _int_or_default(chain.get("max_depth"), cfg.chain_axis_max_depth)
- cfg.chain_axis_max_pairs = _int_or_default(chain.get("max_pairs"), cfg.chain_axis_max_pairs)
- cfg.chain_axis_weight = _float_or_default(chain.get("weight"), cfg.chain_axis_weight)
-
- cfg.axis_alignment_threshold = _float_or_default(
- rules.get("axis_alignment_threshold"), cfg.axis_alignment_threshold
- )
-
- if getattr(args, "strictUniqueMarkerIds", False):
- cfg.strict_unique_marker_ids = True
-
- if getattr(args, "showSkippedConstraints", None) is not None:
- cfg.show_skipped_constraints = bool(args.showSkippedConstraints)
-
- return cfg
-
-
-# ===================================================================
-# Constraint definitions
-# ===================================================================
-
-@dataclass
-class MarkerDistanceConstraint:
- marker_id_a: int
- marker_id_b: int
- link_name: str
- target_distance_m: float
- weight: float = 1.0
- enabled: bool = True
- source: str = "rigid_distance"
-
-
-@dataclass
-class JointAxisConstraint:
- marker_id_parent: int
- marker_id_child: int
- parent_link: str
- child_link: str
- joint_axis: np.ndarray
- target_delta_along_axis_m: float
- weight: float = 1.0
- enabled: bool = True
- source: str = "joint_axis_projection"
-
-
-Constraint = MarkerDistanceConstraint | JointAxisConstraint
-
-
-# ===================================================================
-# Robot parsing
-# ===================================================================
-
-def parse_robot_markers(
- robot_data: Dict[str, Any],
- length_scale: float,
- strict_unique_marker_ids: bool = False
-) -> Tuple[Dict[int, str], Dict[str, List[Dict[str, Any]]], List[str]]:
- """
- Build:
- marker_to_link: marker_id -> link_name
- link_markers: link_name -> [{id, position_m, position_raw, ...}, ...]
- """
- links = robot_data.get("links", {}) or {}
-
- marker_to_link: Dict[int, str] = {}
- link_markers: Dict[str, List[Dict[str, Any]]] = {}
- issues: List[str] = []
-
- seen_global: Dict[int, str] = {}
-
- for link_name, link_data in links.items():
- markers = link_data.get("markers", []) or []
- collected: List[Dict[str, Any]] = []
-
- seen_local: set[int] = set()
-
- for idx, marker in enumerate(markers):
- marker_id = int(marker.get("id", -1))
- pos = marker.get("position", None)
-
- if marker_id < 0 or pos is None or len(pos) != 3:
- issues.append(f"[WARN] link={link_name}: skipped invalid marker entry at index {idx}")
- continue
-
- if marker_id in seen_local:
- msg = f"[WARN] duplicate marker id {marker_id} inside link '{link_name}'"
- if strict_unique_marker_ids:
- raise ValueError(msg)
- issues.append(msg + " -> skipped duplicate inside same link")
- continue
-
- if marker_id in seen_global:
- msg = (
- f"[WARN] duplicate marker id {marker_id} appears in link '{link_name}' "
- f"and already in link '{seen_global[marker_id]}'"
- )
- if strict_unique_marker_ids:
- raise ValueError(msg)
- issues.append(msg + " -> skipped duplicate occurrence")
- continue
-
- seen_local.add(marker_id)
- seen_global[marker_id] = link_name
-
- pos_raw = np.array(pos, dtype=np.float64)
- pos_m = pos_raw * float(length_scale)
-
- collected.append(
- {
- "id": marker_id,
- "name": marker.get("name", f"marker_{marker_id}"),
- "position_raw": pos_raw,
- "position_m": pos_m,
- "normal": np.array(marker.get("normal", [0, 0, 1]), dtype=np.float64),
- "size": marker.get("size", None),
- "spin": marker.get("spin", None),
- }
- )
-
- marker_to_link[marker_id] = link_name
-
- link_markers[link_name] = collected
-
- return marker_to_link, link_markers, issues
-
-
-def get_link_parent_map(robot_data: Dict[str, Any]) -> Dict[str, Optional[str]]:
- links = robot_data.get("links", {}) or {}
- parent_map: Dict[str, Optional[str]] = {}
- for link_name, link_data in links.items():
- parent_map[link_name] = link_data.get("parent", None)
- return parent_map
-
-
-def get_joint_info(robot_data: Dict[str, Any], child_link: str) -> Dict[str, Any]:
- links = robot_data.get("links", {}) or {}
- return (links.get(child_link, {}) or {}).get("jointToParent", {}) or {}
-
-
-def get_joint_axis(robot_data: Dict[str, Any], child_link: str) -> Optional[np.ndarray]:
- joint = get_joint_info(robot_data, child_link)
- axis = joint.get("axis", None)
- if axis is None:
- return None
- axis = np.asarray(axis, dtype=np.float64)
- if safe_norm(axis) < 1e-12:
- return None
- return normalize_vector(axis)
-
-
-# ===================================================================
-# Constraint compilation helpers
-# ===================================================================
-
-def get_enabled_link_rule(
- robot_data: Dict[str, Any],
- link_name: str,
- rule_name: str,
- default_enabled: bool = True
-) -> bool:
- """
- Optional per-link override structure:
-
- "constraint_overrides": {
- "Arm2": {
- "rigid_distance": {"enabled": true},
- "joint_axis_projection": {"enabled": false}
- }
- }
- """
- overrides = robot_data.get("constraint_overrides", {}) or {}
- link_override = overrides.get(link_name, {}) or {}
- rule_override = link_override.get(rule_name, {}) or {}
- if "enabled" in rule_override:
- return bool(rule_override["enabled"])
- return default_enabled
-
-
-def select_anchor_marker_ids(
- markers: List[Dict[str, Any]],
- axis: Optional[np.ndarray] = None,
- max_count: int = 2
-) -> List[int]:
- """
- Deterministic anchor selection:
- - if axis is provided, choose markers with min/max projection on that axis
- - otherwise choose marker(s) closest to centroid and farthest from centroid
-
- Returns up to max_count ids, deduplicated.
- """
- if not markers:
- return []
-
- if len(markers) == 1:
- return [int(markers[0]["id"])]
-
- ids = [int(m["id"]) for m in markers]
- pos = np.stack([np.asarray(m["position_m"], dtype=np.float64) for m in markers], axis=0)
-
- selected: List[int] = []
-
- if axis is not None and safe_norm(axis) > 1e-12:
- a = normalize_vector(axis)
- proj = pos @ a
- min_idx = int(np.argmin(proj))
- max_idx = int(np.argmax(proj))
- selected = [ids[min_idx], ids[max_idx]]
- else:
- centroid = np.mean(pos, axis=0)
- d = np.linalg.norm(pos - centroid, axis=1)
- min_idx = int(np.argmin(d))
- max_idx = int(np.argmax(d))
- selected = [ids[min_idx], ids[max_idx]]
-
- # Fill if needed
- if len(selected) < max_count:
- for mid in ids:
- if mid not in selected:
- selected.append(mid)
- if len(selected) >= max_count:
- break
-
- # Deduplicate while preserving order
- out: List[int] = []
- seen: set[int] = set()
- for mid in selected:
- if mid not in seen:
- seen.add(mid)
- out.append(mid)
- if len(out) >= max_count:
- break
-
- return out
-
-
-def mst_edges_for_link(markers: List[Dict[str, Any]]) -> List[Tuple[int, int]]:
- """
- Prim's algorithm on a complete graph of marker positions.
- Returns n-1 edges connecting all markers with minimal total distance.
- """
- n = len(markers)
- if n < 2:
- return []
-
- ids = [int(m["id"]) for m in markers]
- pos = np.stack([np.asarray(m["position_m"], dtype=np.float64) for m in markers], axis=0)
-
- in_tree = np.zeros(n, dtype=bool)
- in_tree[0] = True
- edges: List[Tuple[int, int]] = []
-
- # pairwise distances
- dist = np.linalg.norm(pos[:, None, :] - pos[None, :, :], axis=2)
-
- for _ in range(n - 1):
- best = None
- best_d = float("inf")
- for i in range(n):
- if not in_tree[i]:
- continue
- for j in range(n):
- if in_tree[j]:
- continue
- d = float(dist[i, j])
- if d < best_d:
- best_d = d
- best = (i, j)
- if best is None:
- break
- i, j = best
- in_tree[j] = True
- edges.append((ids[i], ids[j]))
-
- return edges
-
-
-def compile_rigid_distance_constraints(
- robot_data: Dict[str, Any],
- link_markers: Dict[str, List[Dict[str, Any]]],
- cfg: ConstraintRuleConfig
-) -> List[MarkerDistanceConstraint]:
- constraints: List[MarkerDistanceConstraint] = []
- links = robot_data.get("links", {}) or {}
-
- for link_name, markers in link_markers.items():
- if not get_enabled_link_rule(robot_data, link_name, "rigid_distance", cfg.rigid_distance_enabled):
- continue
-
- if len(markers) < 2:
- continue
-
- mode = cfg.rigid_distance_mode
-
- if mode == "full":
- pairs = [(int(a["id"]), int(b["id"])) for a, b in combinations(markers, 2)]
- elif mode == "star":
- anchor_ids = select_anchor_marker_ids(markers, axis=None, max_count=1)
- anchor_id = anchor_ids[0]
- pairs = []
- for m in markers:
- mid = int(m["id"])
- if mid != anchor_id:
- pairs.append((anchor_id, mid))
- elif mode == "mst":
- pairs = mst_edges_for_link(markers)
- else:
- raise ValueError(f"Unknown rigid_distance_mode='{mode}'. Use mst|star|full.")
-
- pos_map = {int(m["id"]): np.asarray(m["position_m"], dtype=np.float64) for m in markers}
- unique_pairs: List[Tuple[int, int]] = []
- seen_pairs: set[Tuple[int, int]] = set()
-
- for mid_a, mid_b in pairs:
- if mid_a == mid_b:
- continue
- key = tuple(sorted((int(mid_a), int(mid_b))))
- if key in seen_pairs:
- continue
- seen_pairs.add(key)
- unique_pairs.append((int(mid_a), int(mid_b)))
-
- for mid_a, mid_b in unique_pairs:
- pos_a = pos_map[mid_a]
- pos_b = pos_map[mid_b]
- target = float(np.linalg.norm(pos_b - pos_a))
-
- constraints.append(
- MarkerDistanceConstraint(
- marker_id_a=mid_a,
- marker_id_b=mid_b,
- link_name=link_name,
- target_distance_m=target,
- weight=cfg.rigid_distance_weight,
- enabled=True,
- source=f"rigid_distance:{mode}",
- )
- )
-
- return constraints
-
-
-def compile_direct_joint_axis_constraints(
- robot_data: Dict[str, Any],
- link_markers: Dict[str, List[Dict[str, Any]]],
- cfg: ConstraintRuleConfig
-) -> List[JointAxisConstraint]:
- """
- Direct parent-child axis projection constraints.
- We keep them intentionally sparse:
- - choose up to 2 anchors per side (min/max along axis)
- - create cross-product pairs between anchors
- """
- constraints: List[JointAxisConstraint] = []
- links = robot_data.get("links", {}) or {}
-
- for child_link, child_data in links.items():
- parent_link = child_data.get("parent", None)
- if not parent_link:
- continue
-
- if not get_enabled_link_rule(robot_data, child_link, "joint_axis_projection", cfg.joint_axis_enabled):
- continue
-
- joint_axis = get_joint_axis(robot_data, child_link)
- if joint_axis is None:
- continue
-
- axis_id = principal_axis_id(joint_axis, threshold=cfg.axis_alignment_threshold)
- if axis_id is None:
- # Still allow non-principal axes, but keep them with lower confidence:
- axis_id = int(np.argmax(np.abs(joint_axis)))
- axis_vec = np.zeros(3, dtype=np.float64)
- axis_vec[axis_id] = float(np.sign(joint_axis[axis_id]) if abs(joint_axis[axis_id]) > 1e-12 else 1.0)
- axis_vec = normalize_vector(axis_vec)
-
- parent_markers = link_markers.get(parent_link, [])
- child_markers = link_markers.get(child_link, [])
- if len(parent_markers) == 0 or len(child_markers) == 0:
- continue
-
- max_pairs = max(1, int(cfg.joint_axis_max_pairs))
- parent_anchor_ids = select_anchor_marker_ids(parent_markers, axis=axis_vec, max_count=max_pairs)
- child_anchor_ids = select_anchor_marker_ids(child_markers, axis=axis_vec, max_count=max_pairs)
-
- parent_pos = {int(m["id"]): np.asarray(m["position_m"], dtype=np.float64) for m in parent_markers}
- child_pos = {int(m["id"]): np.asarray(m["position_m"], dtype=np.float64) for m in child_markers}
-
- seen: set[Tuple[int, int]] = set()
-
- for mid_p in parent_anchor_ids:
- for mid_c in child_anchor_ids:
- if mid_p == mid_c:
- continue
- key = (mid_p, mid_c)
- if key in seen:
- continue
- seen.add(key)
-
- delta = child_pos[mid_c] - parent_pos[mid_p]
- target = float(np.dot(delta, axis_vec))
-
- constraints.append(
- JointAxisConstraint(
- marker_id_parent=mid_p,
- marker_id_child=mid_c,
- parent_link=parent_link,
- child_link=child_link,
- joint_axis=axis_vec,
- target_delta_along_axis_m=target,
- weight=cfg.joint_axis_weight,
- enabled=True,
- source="joint_axis_projection",
- )
- )
-
- return constraints
-
-
-def ancestors_of(link_name: str, parent_map: Dict[str, Optional[str]], max_depth: int) -> List[str]:
- out = []
- cur = parent_map.get(link_name, None)
- depth = 0
- while cur is not None and depth < max_depth:
- out.append(cur)
- cur = parent_map.get(cur, None)
- depth += 1
- return out
-
-
-def path_axes_consistent(
- robot_data: Dict[str, Any],
- ancestor: str,
- descendant: str,
- parent_map: Dict[str, Optional[str]],
- threshold: float
-) -> Optional[np.ndarray]:
- """
- Check whether the chain from ancestor to descendant is consistent with a single
- principal axis. Returns the axis vector (unit, sign-preserving) if yes.
- """
- # Build path descendant -> ancestor
- chain: List[str] = []
- cur = descendant
- while cur is not None and cur != ancestor:
- chain.append(cur)
- cur = parent_map.get(cur, None)
-
- if cur != ancestor:
- return None # not an ancestor
-
- chain.reverse() # from ancestor child-step to descendant
-
- # Collect axes on each joint in the path
- axes: List[np.ndarray] = []
- for link in chain:
- ax = get_joint_axis(robot_data, link)
- if ax is None:
- return None
- axes.append(ax)
-
- if not axes:
- return None
-
- axis_ids: List[int] = []
- signs: List[float] = []
- for ax in axes:
- aid = principal_axis_id(ax, threshold=threshold)
- if aid is None:
- return None
- axis_ids.append(aid)
- signs.append(float(np.sign(ax[aid])) if abs(ax[aid]) > 1e-12 else 1.0)
-
- if len(set(axis_ids)) != 1:
- return None
-
- axis_id = axis_ids[0]
- sign = 1.0 if np.sum(signs) >= 0 else -1.0
- axis_vec = np.zeros(3, dtype=np.float64)
- axis_vec[axis_id] = sign
- return normalize_vector(axis_vec)
-
-
-def compile_chain_axis_constraints(
- robot_data: Dict[str, Any],
- link_markers: Dict[str, List[Dict[str, Any]]],
- cfg: ConstraintRuleConfig
-) -> List[JointAxisConstraint]:
- """
- Ancestor-descendant projection constraints for chains that preserve a stable
- principal axis over multiple joints.
-
- Example:
- Board -> Base -> Arm1 -> Ellbow
- with all joints x-aligned:
- add Board<->Arm1, Board<->Ellbow, Base<->Ellbow, ...
- """
- constraints: List[JointAxisConstraint] = []
- parent_map = get_link_parent_map(robot_data)
-
- links_with_markers = [ln for ln, mk in link_markers.items() if len(mk) > 0]
- max_depth = max(1, int(cfg.chain_axis_max_depth))
- max_pairs = max(1, int(cfg.chain_axis_max_pairs))
-
- # Cache positions for quick lookup
- pos_cache: Dict[int, np.ndarray] = {}
- for ln, mk in link_markers.items():
- for m in mk:
- pos_cache[int(m["id"])] = np.asarray(m["position_m"], dtype=np.float64)
-
- for descendant in links_with_markers:
- if not get_enabled_link_rule(robot_data, descendant, "chain_axis_projection", cfg.chain_axis_enabled):
- continue
-
- for ancestor in ancestors_of(descendant, parent_map, max_depth=max_depth):
- if ancestor == descendant:
- continue
-
- axis_vec = path_axes_consistent(
- robot_data=robot_data,
- ancestor=ancestor,
- descendant=descendant,
- parent_map=parent_map,
- threshold=cfg.axis_alignment_threshold,
- )
- if axis_vec is None:
- continue
-
- ancestor_markers = link_markers.get(ancestor, [])
- descendant_markers = link_markers.get(descendant, [])
- if len(ancestor_markers) == 0 or len(descendant_markers) == 0:
- continue
-
- anc_anchors = select_anchor_marker_ids(ancestor_markers, axis=axis_vec, max_count=max_pairs)
- des_anchors = select_anchor_marker_ids(descendant_markers, axis=axis_vec, max_count=max_pairs)
-
- seen: set[Tuple[int, int]] = set()
- for mid_a in anc_anchors:
- for mid_b in des_anchors:
- if mid_a == mid_b:
- continue
- key = (mid_a, mid_b)
- if key in seen:
- continue
- seen.add(key)
-
- target = float(np.dot(pos_cache[mid_b] - pos_cache[mid_a], axis_vec))
-
- constraints.append(
- JointAxisConstraint(
- marker_id_parent=mid_a,
- marker_id_child=mid_b,
- parent_link=ancestor,
- child_link=descendant,
- joint_axis=axis_vec,
- target_delta_along_axis_m=target,
- weight=cfg.chain_axis_weight,
- enabled=True,
- source=f"chain_axis_projection:depth{len(ancestors_of(descendant, parent_map, max_depth))}",
- )
- )
-
- return constraints
-
-
-def compile_constraints(
- robot_data: Dict[str, Any],
- length_scale: float,
- cfg: ConstraintRuleConfig
-) -> Tuple[Dict[int, str], Dict[str, List[Dict[str, Any]]], List[Constraint], List[str]]:
- """
- Full rule-based constraint compiler.
- Returns:
- marker_to_link
- link_markers
- constraints
- issues
- """
- marker_to_link, link_markers, issues = parse_robot_markers(
- robot_data,
- length_scale=length_scale,
- strict_unique_marker_ids=cfg.strict_unique_marker_ids,
- )
-
- constraints: List[Constraint] = []
- constraints.extend(compile_rigid_distance_constraints(robot_data, link_markers, cfg))
- constraints.extend(compile_direct_joint_axis_constraints(robot_data, link_markers, cfg))
- constraints.extend(compile_chain_axis_constraints(robot_data, link_markers, cfg))
-
- # De-duplicate identical constraints
- unique_constraints: List[Constraint] = []
- seen_keys: set[Tuple[Any, ...]] = set()
-
- for c in constraints:
- if isinstance(c, MarkerDistanceConstraint):
- key = ("d", min(c.marker_id_a, c.marker_id_b), max(c.marker_id_a, c.marker_id_b), c.link_name, round(c.target_distance_m, 9))
- else:
- key = (
- "j",
- c.parent_link,
- c.child_link,
- c.marker_id_parent,
- c.marker_id_child,
- tuple(np.round(c.joint_axis, 9).tolist()),
- round(c.target_delta_along_axis_m, 9),
- )
- if key in seen_keys:
- continue
- seen_keys.add(key)
- unique_constraints.append(c)
-
- return marker_to_link, link_markers, unique_constraints, issues
-
-
-# ===================================================================
-# Constraint reporting
-# ===================================================================
-
-def print_constraint_summary(constraints: List[Constraint]) -> None:
- num_dist = sum(isinstance(c, MarkerDistanceConstraint) for c in constraints)
- num_joint = sum(isinstance(c, JointAxisConstraint) for c in constraints)
- print(f"[INFO] Constraint summary: total={len(constraints)} distance={num_dist} joint/chain={num_joint}")
-
-
-def print_constraint_list(constraints: List[Constraint]) -> None:
- print("\n[INFO] Constraint list:")
- for idx, constraint in enumerate(constraints):
- if isinstance(constraint, MarkerDistanceConstraint):
- print(
- f" [{idx:03d}] DISTANCE | "
- f"Link='{constraint.link_name}' | "
- f"M{constraint.marker_id_a} <-> M{constraint.marker_id_b} | "
- f"Target={constraint.target_distance_m:.6f} m | "
- f"Weight={constraint.weight} | "
- f"Source={constraint.source}"
- )
- else:
- axis_str = np.array2string(constraint.joint_axis, precision=3, suppress_small=True)
- print(
- f" [{idx:03d}] JOINT_AXIS | "
- f"{constraint.parent_link}(M{constraint.marker_id_parent}) -> "
- f"{constraint.child_link}(M{constraint.marker_id_child}) | "
- f"Axis={axis_str} | "
- f"TargetDelta={constraint.target_delta_along_axis_m:.6f} m | "
- f"Weight={constraint.weight} | "
- f"Source={constraint.source}"
- )
-
-
-def print_constraints_with_errors(
- title: str,
- constraints: List[Constraint],
- positions: Dict[int, np.ndarray],
- show_skipped: bool = True
-) -> None:
- print(f"\n[INFO] {title}")
-
- active = 0
- skipped = 0
-
- for idx, constraint in enumerate(constraints):
- if isinstance(constraint, MarkerDistanceConstraint):
- if constraint.marker_id_a not in positions or constraint.marker_id_b not in positions:
- skipped += 1
- if show_skipped:
- print(
- f" [{idx:03d}] DISTANCE | "
- f"M{constraint.marker_id_a} <-> M{constraint.marker_id_b} | SKIPPED (missing marker)"
- )
- continue
-
- pos_a = positions[constraint.marker_id_a]
- pos_b = positions[constraint.marker_id_b]
- actual = float(np.linalg.norm(pos_b - pos_a))
- error = actual - constraint.target_distance_m
- active += 1
-
- print(
- f" [{idx:03d}] DISTANCE | "
- f"Link='{constraint.link_name}' | "
- f"M{constraint.marker_id_a} <-> M{constraint.marker_id_b} | "
- f"target={constraint.target_distance_m*1000:.2f} mm | "
- f"actual={actual*1000:.2f} mm | "
- f"error={error*1000:+.2f} mm"
- )
-
- elif isinstance(constraint, JointAxisConstraint):
- if constraint.marker_id_parent not in positions or constraint.marker_id_child not in positions:
- skipped += 1
- if show_skipped:
- print(
- f" [{idx:03d}] JOINT_AXIS | "
- f"M{constraint.marker_id_parent} -> M{constraint.marker_id_child} | SKIPPED (missing marker)"
- )
- continue
-
- pos_parent = positions[constraint.marker_id_parent]
- pos_child = positions[constraint.marker_id_child]
- delta = pos_child - pos_parent
- actual = float(np.dot(delta, constraint.joint_axis))
- error = actual - constraint.target_delta_along_axis_m
- active += 1
-
- axis_str = np.array2string(constraint.joint_axis, precision=2, suppress_small=True)
- print(
- f" [{idx:03d}] JOINT_AXIS | "
- f"{constraint.parent_link}(M{constraint.marker_id_parent}) -> "
- f"{constraint.child_link}(M{constraint.marker_id_child}) | "
- f"axis={axis_str} | "
- f"target={constraint.target_delta_along_axis_m*1000:.2f} mm | "
- f"actual={actual*1000:.2f} mm | "
- f"error={error*1000:+.2f} mm"
- )
-
- print(f"[INFO] Active constraints: {active} | Skipped: {skipped}")
-
-
-# ===================================================================
-# Multi-view loading
-# ===================================================================
-
-def load_observations_and_poses(
- detection_files: List[str],
- pose_files: List[str]
-) -> Tuple[Dict[int, List[Tuple[int, np.ndarray]]], List[Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]], List[Dict[str, Any]]]:
- """Load detections and camera poses."""
- if len(detection_files) != len(pose_files):
- raise ValueError(f"Mismatch: {len(detection_files)} detections vs {len(pose_files)} poses")
-
- marker_observations: Dict[int, List[Tuple[int, np.ndarray]]] = {}
- cameras: List[Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]] = []
- obs_metadata: List[Dict[str, Any]] = []
-
- for cam_idx, (det_file, pose_file) in enumerate(zip(detection_files, pose_files)):
- det = load_json(det_file)
- pose_data = load_json(pose_file)
-
- cam_section = det.get("camera", {}) or {}
- K = np.array(cam_section.get("camera_matrix", []), dtype=np.float64).reshape(3, 3)
- D = np.array(cam_section.get("distortion_coefficients", []), dtype=np.float64).reshape(-1, 1)
-
- pose_section = pose_data.get("camera_pose", {}) or {}
- world_to_cam = pose_section.get("world_to_camera", {}) or {}
- R_wc = np.array(world_to_cam.get("rotation_matrix", []), dtype=np.float64).reshape(3, 3)
- t_wc = np.array(world_to_cam.get("translation_m", []), dtype=np.float64).reshape(3)
-
- cameras.append((K, D, R_wc, t_wc))
-
- detections = det.get("detections", []) or []
- for det_obj in detections:
- marker_id = int(det_obj.get("marker_id", -1))
- if marker_id < 0:
- continue
-
- center_px = np.array(det_obj.get("center_px", []), dtype=np.float64)
- if center_px.shape != (2,):
- continue
-
- pts = center_px.reshape(1, 1, 2).astype(np.float32)
- und = cv2.undistortPoints(pts, K.astype(np.float32), D.astype(np.float32), P=None)
- norm_coords = und.reshape(2).astype(np.float64)
-
- marker_observations.setdefault(marker_id, []).append((cam_idx, norm_coords))
-
- obs_metadata.append(
- {
- "detection_file": det_file,
- "pose_file": pose_file,
- "num_detections": len(detections),
- }
- )
-
- return marker_observations, cameras, obs_metadata
-
-
-# ===================================================================
-# Initial triangulation
-# ===================================================================
-
-def triangulate_marker_initial(
- marker_id: int,
- observations: List[Tuple[int, np.ndarray]],
- cameras: List[Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]]
-) -> Optional[np.ndarray]:
- """
- Triangulate using the best baseline pair among all available observations.
- This is more stable than using just the first two views.
- """
- if len(observations) < 2:
- return None
-
- best_pair = None
- best_baseline = -1.0
-
- for (cam_i, _), (cam_j, _) in combinations(observations, 2):
- K1, D1, R1, t1 = cameras[cam_i]
- K2, D2, R2, t2 = cameras[cam_j]
- c1 = camera_center_from_world_to_cam(R1, t1)
- c2 = camera_center_from_world_to_cam(R2, t2)
- baseline = float(np.linalg.norm(c2 - c1))
- if baseline > best_baseline:
- best_baseline = baseline
- best_pair = (cam_i, cam_j)
-
- if best_pair is None:
- return None
-
- cam_i, cam_j = best_pair
- norm_coords_i = None
- norm_coords_j = None
- for ci, coords in observations:
- if ci == cam_i:
- norm_coords_i = coords
- elif ci == cam_j:
- norm_coords_j = coords
-
- if norm_coords_i is None or norm_coords_j is None:
- return None
-
- K1, D1, R1, t1 = cameras[cam_i]
- K2, D2, R2, t2 = cameras[cam_j]
-
- x1_px = K1[0, 0] * norm_coords_i[0] + K1[0, 2]
- y1_px = K1[1, 1] * norm_coords_i[1] + K1[1, 2]
- x2_px = K2[0, 0] * norm_coords_j[0] + K2[0, 2]
- y2_px = K2[1, 1] * norm_coords_j[1] + K2[1, 2]
-
- P1 = K1 @ np.hstack([R1, t1.reshape(3, 1)])
- P2 = K2 @ np.hstack([R2, t2.reshape(3, 1)])
-
- try:
- X_h = cv2.triangulatePoints(
- P1,
- P2,
- np.array([[x1_px], [y1_px]], dtype=np.float64),
- np.array([[x2_px], [y2_px]], dtype=np.float64),
- )
- X = (X_h[:3] / X_h[3]).reshape(3).astype(np.float64)
- if not np.all(np.isfinite(X)):
- return None
- return X
- except Exception:
- return None
-
-
-def initial_triangulation(
- marker_observations: Dict[int, List[Tuple[int, np.ndarray]]],
- cameras: List[Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]]
-) -> Dict[int, np.ndarray]:
- triangulated: Dict[int, np.ndarray] = {}
- for marker_id, observations in marker_observations.items():
- X = triangulate_marker_initial(marker_id, observations, cameras)
- if X is not None:
- triangulated[marker_id] = X
- return triangulated
-
-
-# ===================================================================
-# Residuals / optimization
-# ===================================================================
-
-def bundle_adjustment_residuals(
- marker_positions_flat: np.ndarray,
- marker_ids: List[int],
- marker_observations: Dict[int, List[Tuple[int, np.ndarray]]],
- cameras: List[Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]],
- constraints: List[Constraint],
- lambda_constraint: float = 100.0
-) -> np.ndarray:
- marker_dict: Dict[int, np.ndarray] = {}
- for i, marker_id in enumerate(marker_ids):
- marker_dict[marker_id] = marker_positions_flat[i * 3:(i + 1) * 3]
-
- residuals: List[float] = []
-
- # Reprojection residuals in normalized coordinates
- for marker_id, observations in marker_observations.items():
- if marker_id not in marker_dict:
- continue
-
- X_world = marker_dict[marker_id]
- for cam_idx, norm_coords_obs in observations:
- K, D, R_wc, t_wc = cameras[cam_idx]
- X_cam = R_wc @ X_world + t_wc
- if X_cam[2] > 1e-6:
- proj_norm = X_cam[:2] / X_cam[2]
- r = proj_norm - norm_coords_obs
- residuals.append(float(r[0]))
- residuals.append(float(r[1]))
-
- # Constraint residuals
- for constraint in constraints:
- if isinstance(constraint, MarkerDistanceConstraint):
- if constraint.marker_id_a in marker_dict and constraint.marker_id_b in marker_dict:
- pos_a = marker_dict[constraint.marker_id_a]
- pos_b = marker_dict[constraint.marker_id_b]
- actual_dist = float(np.linalg.norm(pos_b - pos_a))
- residuals.append(
- (actual_dist - constraint.target_distance_m) * constraint.weight * lambda_constraint
- )
-
- elif isinstance(constraint, JointAxisConstraint):
- if constraint.marker_id_parent in marker_dict and constraint.marker_id_child in marker_dict:
- pos_parent = marker_dict[constraint.marker_id_parent]
- pos_child = marker_dict[constraint.marker_id_child]
- delta = pos_child - pos_parent
- actual_delta = float(np.dot(delta, constraint.joint_axis))
- residuals.append(
- (actual_delta - constraint.target_delta_along_axis_m) * constraint.weight * lambda_constraint
- )
-
- return np.asarray(residuals, dtype=np.float64)
-
-
-def optimize_with_constraints(
- initial_positions: Dict[int, np.ndarray],
- marker_observations: Dict[int, List[Tuple[int, np.ndarray]]],
- cameras: List[Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]],
- constraints: List[Constraint],
- lambda_constraint: float = 100.0,
- max_iterations: int = 50
-) -> Dict[int, np.ndarray]:
- try:
- from scipy.optimize import least_squares
- except ImportError:
- print("[WARN] scipy not available, skipping optimization.")
- return initial_positions
-
- marker_ids = sorted(initial_positions.keys())
- if not marker_ids:
- return {}
-
- x0 = np.concatenate([initial_positions[mid] for mid in marker_ids])
-
- def residuals_fn(x: np.ndarray) -> np.ndarray:
- return bundle_adjustment_residuals(
- x, marker_ids, marker_observations, cameras, constraints, lambda_constraint
- )
-
- print(f"[INFO] Starting optimization with {len(x0)} variables and {len(constraints)} constraints...")
-
- result = least_squares(
- residuals_fn,
- x0,
- max_nfev=max_iterations * max(1, len(marker_ids)),
- verbose=1,
- )
-
- optimized = {}
- for i, marker_id in enumerate(marker_ids):
- optimized[marker_id] = result.x[i * 3:(i + 1) * 3]
-
- print(f"[INFO] Optimization complete. Final cost: {float(np.sum(result.fun ** 2)):.6f}")
- return optimized
-
-
-# ===================================================================
-# Main
-# ===================================================================
-
-def main() -> None:
- parser = argparse.ArgumentParser(
- description="Multi-view bundle adjustment with rule-based geometric constraints"
- )
- parser.add_argument(
- "-det", "--detections",
- action="append",
- required=True,
- help="*_aruco_detection.json files"
- )
-
- parser.add_argument(
- "-pose", "--poses",
- action="append",
- required=True,
- help="*_camera_pose.json files"
- )
- parser.add_argument(
- "-robot", "--robot",
- required=True,
- help="robot.json"
- )
- parser.add_argument(
- "-outDir", "--outDir",
- default=None,
- help="Output directory"
- )
- parser.add_argument(
- "-lambdaWeight", "--lambdaWeight",
- type=float,
- default=100.0,
- help="Constraint weight multiplier"
- )
- parser.add_argument(
- "--strictUniqueMarkerIds",
- action="store_true",
- help="Fail if a marker ID appears more than once in robot.json"
- )
- parser.add_argument(
- "--showSkippedConstraints",
- action="store_true",
- help="Print skipped constraints in the report"
- )
- parser.add_argument(
- "--noShowSkippedConstraints",
- action="store_true",
- help="Hide skipped constraints in the report"
- )
- parser.add_argument(
- "--saveConstraintReport",
- action="store_true",
- help="Save constraint report JSON files"
- )
-
- args = parser.parse_args()
-
- if args.showSkippedConstraints and args.noShowSkippedConstraints:
- print("[ERROR] Choose only one of --showSkippedConstraints or --noShowSkippedConstraints")
- sys.exit(1)
-
- if len(args.detections) != len(args.poses):
- print(f"[ERROR] Mismatch: {len(args.detections)} detection files vs {len(args.poses)} pose files")
- sys.exit(1)
-
- robot_data = load_json(args.robot)
- length_scale = get_length_scale(robot_data)
- cfg = load_constraint_rule_config(robot_data, args)
-
- if args.noShowSkippedConstraints:
- cfg.show_skipped_constraints = False
- elif args.showSkippedConstraints:
- cfg.show_skipped_constraints = True
-
- print("[STEP 1] Compile constraints from robot.json structure...")
- marker_to_link, link_markers, constraints, issues = compile_constraints(robot_data, length_scale, cfg)
-
- for issue in issues:
- print(issue)
-
- print(f"[INFO] Links with markers: {sum(1 for v in link_markers.values() if len(v) > 0)}")
- print(f"[INFO] Unique marker IDs: {len(marker_to_link)}")
- print_constraint_summary(constraints)
- print_constraint_list(constraints)
-
- print("\n[STEP 2] Load observations and camera poses...")
- marker_observations, cameras, obs_metadata = load_observations_and_poses(args.detections, args.poses)
- print(f"[INFO] {len(cameras)} cameras, {len(marker_observations)} observed markers")
- print(f"[INFO] Detection files loaded: {len(obs_metadata)}")
-
- print("\n[STEP 3] Initial triangulation...")
- initial_pos = initial_triangulation(marker_observations, cameras)
- print(f"[INFO] Triangulated {len(initial_pos)} markers")
-
- out_dir = args.outDir or os.path.dirname(args.detections[0]) or "."
- os.makedirs(resolve_path(out_dir), exist_ok=True)
-
- # Save initial solution
- initial_output_markers = []
- for marker_id, position in sorted(initial_pos.items()):
- initial_output_markers.append(
- {
- "marker_id": int(marker_id),
- "position_m": [float(x) for x in position],
- "position_mm": [float(x * 1000.0) for x in position],
- "link": marker_to_link.get(marker_id, "unknown"),
- }
- )
-
- initial_output = {
- "schema_version": "1.1",
- "stage": "initial_triangulation",
- "created_utc": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
- "summary": {
- "num_cameras": len(cameras),
- "num_markers": len(initial_pos),
- "num_constraints": len(constraints),
- },
- "markers": initial_output_markers,
- }
-
- initial_out_file = os.path.join(out_dir, "aruco_positions_initial.json")
- save_json(initial_out_file, initial_output)
- print(f"[INFO] Initial triangulation saved to {initial_out_file}")
-
- print_constraints_with_errors(
- "Constraint list BEFORE optimization",
- constraints,
- initial_pos,
- show_skipped=cfg.show_skipped_constraints,
- )
-
- print("\n[STEP 4] Bundle adjustment with constraints...")
- optimized_pos = optimize_with_constraints(
- initial_pos,
- marker_observations,
- cameras,
- constraints,
- lambda_constraint=args.lambdaWeight,
- )
-
- print_constraints_with_errors(
- "Constraint list AFTER optimization",
- constraints,
- optimized_pos,
- show_skipped=cfg.show_skipped_constraints,
- )
-
- output_markers = []
- for marker_id, position in sorted(optimized_pos.items()):
- output_markers.append(
- {
- "marker_id": int(marker_id),
- "position_m": [float(x) for x in position],
- "position_mm": [float(x * 1000.0) for x in position],
- "link": marker_to_link.get(marker_id, "unknown"),
- }
- )
-
- output = {
- "schema_version": "1.1",
- "created_utc": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
- "summary": {
- "num_cameras": len(cameras),
- "num_markers": len(optimized_pos),
- "num_constraints": len(constraints),
- },
- "markers": output_markers,
- }
-
- out_file = os.path.join(out_dir, "aruco_positions_optimized.json")
- save_json(out_file, output)
- print(f"\n[INFO] Saved to {out_file}")
-
- if args.saveConstraintReport:
- report = {
- "schema_version": "1.0",
- "created_utc": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
- "summary": {
- "num_constraints": len(constraints),
- "num_links_with_markers": sum(1 for v in link_markers.values() if len(v) > 0),
- "num_observed_markers": len(marker_observations),
- "num_triangulated_markers": len(initial_pos),
- "num_optimized_markers": len(optimized_pos),
- },
- "constraints": [],
- }
- for c in constraints:
- if isinstance(c, MarkerDistanceConstraint):
- report["constraints"].append(
- {
- "kind": "distance",
- "link_name": c.link_name,
- "marker_id_a": c.marker_id_a,
- "marker_id_b": c.marker_id_b,
- "target_distance_m": c.target_distance_m,
- "weight": c.weight,
- "source": c.source,
- }
- )
- else:
- report["constraints"].append(
- {
- "kind": "joint_axis",
- "parent_link": c.parent_link,
- "child_link": c.child_link,
- "marker_id_parent": c.marker_id_parent,
- "marker_id_child": c.marker_id_child,
- "joint_axis": [float(x) for x in c.joint_axis],
- "target_delta_along_axis_m": c.target_delta_along_axis_m,
- "weight": c.weight,
- "source": c.source,
- }
- )
- report_file = os.path.join(out_dir, "constraint_report.json")
- save_json(report_file, report)
- print(f"[INFO] Constraint report saved to {report_file}")
-
-
-if __name__ == "__main__":
- main()
diff --git a/pipeline/3_multiview_bundle_adjustment_v3.py b/pipeline/3_multiview_bundle_adjustment_v3.py
deleted file mode 100644
index 7376326..0000000
--- a/pipeline/3_multiview_bundle_adjustment_v3.py
+++ /dev/null
@@ -1,1466 +0,0 @@
-#!/usr/bin/env python3
-"""
-3_multiview_bundle_adjustment_v3.py
-
-Multi-view ArUco marker position optimization with rule-based geometric constraints.
-
-Mathematical model
-------------------
-We estimate 3D marker positions X_i ∈ R^3 by minimizing a weighted least-squares objective
-
- E(X) = Σ_i Σ_c w_ic || π_c(X_i) - u_ic ||^2
- + λ_d Σ_j w_j^d || g_j^d(X) ||^2
- + λ_a Σ_k w_k^a || g_k^a(X) ||^2
-
-where:
-- u_ic are observed normalized image coordinates of marker i in camera c
-- π_c(.) is the normalized reprojection model for camera c
-- w_ic are observation weights (quality-based)
-- g_j^d are rigid-link distance constraints
-- g_k^a are joint-axis / chain-axis projection constraints
-
-Important design choices:
-- robot.json is used as a kinematic description, not as a direct source of marker positions.
-- constraint families are explicit and switchable.
-- marker observation weights are based on available measurement quality cues
- (e.g. detection confidence, observed pixel size if present, marker size prior,
- and initial range from the camera); orientation information is treated cautiously.
-- optimization is single-stage in this version to keep comparisons reproducible.
- If a second refinement stage is needed later, it can be added as a separate version.
-
-Dependencies:
- numpy, opencv-python, scipy (optional for optimization)
-
-Example:
- python 3_multiview_bundle_adjustment_v3.py ^
- -det cam1_aruco_detection.json cam2_aruco_detection.json cam3_aruco_detection.json ^
- -pose cam1_camera_pose.json cam2_camera_pose.json cam3_camera_pose.json ^
- -robot robot.json ^
- -lambdaWeight 100.0
-"""
-
-from __future__ import annotations
-
-import argparse
-import json
-import os
-import sys
-import time
-from dataclasses import dataclass
-from itertools import combinations
-from typing import Any, Dict, List, Optional, Tuple
-
-import cv2
-import numpy as np
-
-
-# ===================================================================
-# Path / JSON helpers
-# ===================================================================
-
-def resolve_path(path: str) -> str:
- path = os.path.expanduser(path)
- if os.path.isabs(path):
- return path
- return os.path.abspath(path)
-
-
-def load_json(path: str) -> Dict[str, Any]:
- with open(resolve_path(path), "r", encoding="utf-8") as f:
- return json.load(f)
-
-
-def save_json(path: str, data: Dict[str, Any]) -> None:
- with open(resolve_path(path), "w", encoding="utf-8") as f:
- json.dump(data, f, indent=2)
-
-
-# ===================================================================
-# Units
-# ===================================================================
-
-def get_length_scale(robot_data: Dict[str, Any]) -> float:
- units = robot_data.get("units", {}) or {}
- length_unit = str(units.get("length", "")).strip().lower()
- if length_unit in ("mm", "millimeter", "millimeters"):
- return 1.0 / 1000.0
- if length_unit in ("cm", "centimeter", "centimeters"):
- return 1.0 / 100.0
- return 1.0
-
-
-# ===================================================================
-# Small geometry helpers
-# ===================================================================
-
-def safe_norm(v: np.ndarray, eps: float = 1e-12) -> float:
- return float(np.linalg.norm(v) + eps)
-
-
-def normalize_vector(v: np.ndarray, eps: float = 1e-12) -> np.ndarray:
- return np.asarray(v, dtype=np.float64) / safe_norm(v, eps)
-
-
-def clamp(v: float, lo: float, hi: float) -> float:
- return float(max(lo, min(hi, v)))
-
-
-def principal_axis_id(axis: np.ndarray, threshold: float = 0.95) -> Optional[int]:
- """Return 0,1,2 for x,y,z if axis is close enough to a principal axis."""
- a = normalize_vector(np.asarray(axis, dtype=np.float64))
- idx = int(np.argmax(np.abs(a)))
- if abs(a[idx]) >= threshold:
- return idx
- return None
-
-
-def camera_center_from_world_to_cam(R_wc: np.ndarray, t_wc: np.ndarray) -> np.ndarray:
- """world_to_camera: X_cam = R_wc * X_world + t_wc; camera center is -R^T t."""
- return -R_wc.T @ t_wc
-
-
-def principal_axis_vector(axis: np.ndarray) -> np.ndarray:
- """Convert a near-principal axis to an exact signed principal axis vector."""
- a = normalize_vector(axis)
- idx = int(np.argmax(np.abs(a)))
- out = np.zeros(3, dtype=np.float64)
- out[idx] = 1.0 if a[idx] >= 0 else -1.0
- return normalize_vector(out)
-
-
-# ===================================================================
-# Configuration
-# ===================================================================
-
-@dataclass
-class ConstraintRuleConfig:
- rigid_distance_enabled: bool = True
- rigid_distance_mode: str = "mst" # mst | star | full
- rigid_distance_weight: float = 1.0
-
- joint_axis_enabled: bool = True
- joint_axis_max_pairs: int = 2
- joint_axis_weight: float = 0.5
-
- chain_axis_enabled: bool = True
- chain_axis_max_depth: int = 3
- chain_axis_max_pairs: int = 2
- chain_axis_weight: float = 0.3
-
- axis_alignment_threshold: float = 0.95
-
- strict_unique_marker_ids: bool = False
- show_skipped_constraints: bool = True
-
- enable_observation_weights: bool = True
- weight_floor: float = 0.30
- weight_ceiling: float = 3.00
- ref_distance_m: float = 0.75
- ref_marker_size_px: float = 50.0
- use_detection_confidence: bool = True
- use_detection_size_px: bool = True
- use_initial_range: bool = True
- use_marker_size_prior: bool = True
-
-
-def _bool_or_default(value: Any, default: bool) -> bool:
- if value is None:
- return default
- return bool(value)
-
-
-def _float_or_default(value: Any, default: float) -> float:
- if value is None:
- return default
- return float(value)
-
-
-def _int_or_default(value: Any, default: int) -> int:
- if value is None:
- return default
- return int(value)
-
-
-def load_constraint_rule_config(robot_data: Dict[str, Any], args: argparse.Namespace) -> ConstraintRuleConfig:
- rules = robot_data.get("constraint_rules", {}) or {}
-
- cfg = ConstraintRuleConfig()
- rigid = rules.get("rigid_distance", {}) or {}
- joint = rules.get("joint_axis_projection", {}) or {}
- chain = rules.get("chain_axis_projection", {}) or {}
- obs = rules.get("observation_weights", {}) or {}
-
- cfg.rigid_distance_enabled = _bool_or_default(rigid.get("enabled"), cfg.rigid_distance_enabled)
- cfg.rigid_distance_mode = str(rigid.get("mode", cfg.rigid_distance_mode)).strip().lower()
- cfg.rigid_distance_weight = _float_or_default(rigid.get("weight"), cfg.rigid_distance_weight)
-
- cfg.joint_axis_enabled = _bool_or_default(joint.get("enabled"), cfg.joint_axis_enabled)
- cfg.joint_axis_max_pairs = _int_or_default(joint.get("max_pairs"), cfg.joint_axis_max_pairs)
- cfg.joint_axis_weight = _float_or_default(joint.get("weight"), cfg.joint_axis_weight)
-
- cfg.chain_axis_enabled = _bool_or_default(chain.get("enabled"), cfg.chain_axis_enabled)
- cfg.chain_axis_max_depth = _int_or_default(chain.get("max_depth"), cfg.chain_axis_max_depth)
- cfg.chain_axis_max_pairs = _int_or_default(chain.get("max_pairs"), cfg.chain_axis_max_pairs)
- cfg.chain_axis_weight = _float_or_default(chain.get("weight"), cfg.chain_axis_weight)
-
- cfg.axis_alignment_threshold = _float_or_default(
- rules.get("axis_alignment_threshold"), cfg.axis_alignment_threshold
- )
-
- cfg.enable_observation_weights = _bool_or_default(obs.get("enabled"), cfg.enable_observation_weights)
- cfg.weight_floor = _float_or_default(obs.get("weight_floor"), cfg.weight_floor)
- cfg.weight_ceiling = _float_or_default(obs.get("weight_ceiling"), cfg.weight_ceiling)
- cfg.ref_distance_m = _float_or_default(obs.get("ref_distance_m"), cfg.ref_distance_m)
- cfg.ref_marker_size_px = _float_or_default(obs.get("ref_marker_size_px"), cfg.ref_marker_size_px)
- cfg.use_detection_confidence = _bool_or_default(obs.get("use_detection_confidence"), cfg.use_detection_confidence)
- cfg.use_detection_size_px = _bool_or_default(obs.get("use_detection_size_px"), cfg.use_detection_size_px)
- cfg.use_initial_range = _bool_or_default(obs.get("use_initial_range"), cfg.use_initial_range)
- cfg.use_marker_size_prior = _bool_or_default(obs.get("use_marker_size_prior"), cfg.use_marker_size_prior)
-
- if getattr(args, "strictUniqueMarkerIds", False):
- cfg.strict_unique_marker_ids = True
- if getattr(args, "showSkippedConstraints", False):
- cfg.show_skipped_constraints = True
- if getattr(args, "noShowSkippedConstraints", False):
- cfg.show_skipped_constraints = False
-
- return cfg
-
-
-# ===================================================================
-# Observation / constraint definitions
-# ===================================================================
-
-@dataclass
-class Observation:
- cam_idx: int
- norm_coords: np.ndarray
- meta: Dict[str, Any]
-
-
-@dataclass
-class MarkerDistanceConstraint:
- marker_id_a: int
- marker_id_b: int
- link_name: str
- target_distance_m: float
- weight: float = 1.0
- enabled: bool = True
- source: str = "rigid_distance"
-
-
-@dataclass
-class JointAxisConstraint:
- marker_id_parent: int
- marker_id_child: int
- parent_link: str
- child_link: str
- joint_axis: np.ndarray
- target_delta_along_axis_m: float
- weight: float = 1.0
- enabled: bool = True
- source: str = "joint_axis_projection"
-
-
-Constraint = MarkerDistanceConstraint | JointAxisConstraint
-
-
-# ===================================================================
-# Robot parsing
-# ===================================================================
-
-def parse_robot_markers(
- robot_data: Dict[str, Any],
- length_scale: float,
- strict_unique_marker_ids: bool = False
-) -> Tuple[Dict[int, str], Dict[str, List[Dict[str, Any]]], List[str], Dict[int, Dict[str, Any]]]:
- links = robot_data.get("links", {}) or {}
-
- marker_to_link: Dict[int, str] = {}
- link_markers: Dict[str, List[Dict[str, Any]]] = {}
- issues: List[str] = []
- marker_meta: Dict[int, Dict[str, Any]] = {}
-
- seen_global: Dict[int, str] = {}
-
- for link_name, link_data in links.items():
- markers = link_data.get("markers", []) or []
- collected: List[Dict[str, Any]] = []
- seen_local: set[int] = set()
-
- for idx, marker in enumerate(markers):
- marker_id = int(marker.get("id", -1))
- pos = marker.get("position", None)
-
- if marker_id < 0 or pos is None or len(pos) != 3:
- issues.append(f"[WARN] link={link_name}: skipped invalid marker entry at index {idx}")
- continue
-
- if marker_id in seen_local:
- msg = f"[WARN] duplicate marker id {marker_id} inside link '{link_name}'"
- if strict_unique_marker_ids:
- raise ValueError(msg)
- issues.append(msg + " -> skipped duplicate inside same link")
- continue
-
- if marker_id in seen_global:
- msg = (
- f"[WARN] duplicate marker id {marker_id} appears in link '{link_name}' "
- f"and already in link '{seen_global[marker_id]}'"
- )
- if strict_unique_marker_ids:
- raise ValueError(msg)
- issues.append(msg + " -> skipped duplicate occurrence")
- continue
-
- seen_local.add(marker_id)
- seen_global[marker_id] = link_name
-
- pos_raw = np.array(pos, dtype=np.float64)
- pos_m = pos_raw * float(length_scale)
-
- item = {
- "id": marker_id,
- "name": marker.get("name", f"marker_{marker_id}"),
- "position_raw": pos_raw,
- "position_m": pos_m,
- "normal": np.array(marker.get("normal", [0, 0, 1]), dtype=np.float64),
- "size": marker.get("size", None),
- "spin": marker.get("spin", None),
- }
- collected.append(item)
- marker_to_link[marker_id] = link_name
- marker_meta[marker_id] = {
- "link_name": link_name,
- "name": item["name"],
- "position_m": pos_m,
- "normal": item["normal"],
- "size": item["size"],
- "spin": item["spin"],
- }
-
- link_markers[link_name] = collected
-
- return marker_to_link, link_markers, issues, marker_meta
-
-
-def get_link_parent_map(robot_data: Dict[str, Any]) -> Dict[str, Optional[str]]:
- links = robot_data.get("links", {}) or {}
- return {link_name: (link_data.get("parent", None)) for link_name, link_data in links.items()}
-
-
-def get_joint_info(robot_data: Dict[str, Any], child_link: str) -> Dict[str, Any]:
- links = robot_data.get("links", {}) or {}
- return (links.get(child_link, {}) or {}).get("jointToParent", {}) or {}
-
-
-def get_joint_axis(robot_data: Dict[str, Any], child_link: str) -> Optional[np.ndarray]:
- joint = get_joint_info(robot_data, child_link)
- axis = joint.get("axis", None)
- if axis is None:
- return None
- axis = np.asarray(axis, dtype=np.float64)
- if safe_norm(axis) < 1e-12:
- return None
- return normalize_vector(axis)
-
-
-def get_vision_marker_size_default(robot_data: Dict[str, Any]) -> float:
- vision = robot_data.get("vision_config", {}) or {}
- ms = vision.get("MarkerSize", None)
- if ms is None:
- return 0.025
- return float(ms)
-
-
-# ===================================================================
-# Constraint compilation helpers
-# ===================================================================
-
-def get_enabled_link_rule(
- robot_data: Dict[str, Any],
- link_name: str,
- rule_name: str,
- default_enabled: bool = True
-) -> bool:
- overrides = robot_data.get("constraint_overrides", {}) or {}
- link_override = overrides.get(link_name, {}) or {}
- rule_override = link_override.get(rule_name, {}) or {}
- if "enabled" in rule_override:
- return bool(rule_override["enabled"])
- return default_enabled
-
-
-def select_anchor_marker_ids(
- markers: List[Dict[str, Any]],
- axis: Optional[np.ndarray] = None,
- max_count: int = 2
-) -> List[int]:
- if not markers:
- return []
- if len(markers) == 1:
- return [int(markers[0]["id"])]
-
- ids = [int(m["id"]) for m in markers]
- pos = np.stack([np.asarray(m["position_m"], dtype=np.float64) for m in markers], axis=0)
-
- selected: List[int] = []
-
- if axis is not None and safe_norm(axis) > 1e-12:
- a = normalize_vector(axis)
- proj = pos @ a
- min_idx = int(np.argmin(proj))
- max_idx = int(np.argmax(proj))
- selected = [ids[min_idx], ids[max_idx]]
- else:
- centroid = np.mean(pos, axis=0)
- d = np.linalg.norm(pos - centroid, axis=1)
- min_idx = int(np.argmin(d))
- max_idx = int(np.argmax(d))
- selected = [ids[min_idx], ids[max_idx]]
-
- if len(selected) < max_count:
- for mid in ids:
- if mid not in selected:
- selected.append(mid)
- if len(selected) >= max_count:
- break
-
- out: List[int] = []
- seen: set[int] = set()
- for mid in selected:
- if mid not in seen:
- seen.add(mid)
- out.append(mid)
- if len(out) >= max_count:
- break
- return out
-
-
-def mst_edges_for_link(markers: List[Dict[str, Any]]) -> List[Tuple[int, int]]:
- n = len(markers)
- if n < 2:
- return []
-
- ids = [int(m["id"]) for m in markers]
- pos = np.stack([np.asarray(m["position_m"], dtype=np.float64) for m in markers], axis=0)
- in_tree = np.zeros(n, dtype=bool)
- in_tree[0] = True
- edges: List[Tuple[int, int]] = []
- dist = np.linalg.norm(pos[:, None, :] - pos[None, :, :], axis=2)
-
- for _ in range(n - 1):
- best = None
- best_d = float("inf")
- for i in range(n):
- if not in_tree[i]:
- continue
- for j in range(n):
- if in_tree[j]:
- continue
- d = float(dist[i, j])
- if d < best_d:
- best_d = d
- best = (i, j)
- if best is None:
- break
- i, j = best
- in_tree[j] = True
- edges.append((ids[i], ids[j]))
- return edges
-
-
-def compile_rigid_distance_constraints(
- robot_data: Dict[str, Any],
- link_markers: Dict[str, List[Dict[str, Any]]],
- cfg: ConstraintRuleConfig
-) -> List[MarkerDistanceConstraint]:
- constraints: List[MarkerDistanceConstraint] = []
-
- for link_name, markers in link_markers.items():
- if not get_enabled_link_rule(robot_data, link_name, "rigid_distance", cfg.rigid_distance_enabled):
- continue
- if len(markers) < 2:
- continue
-
- mode = cfg.rigid_distance_mode
- if mode == "full":
- pairs = [(int(a["id"]), int(b["id"])) for a, b in combinations(markers, 2)]
- elif mode == "star":
- anchor_ids = select_anchor_marker_ids(markers, axis=None, max_count=1)
- anchor_id = anchor_ids[0]
- pairs = []
- for m in markers:
- mid = int(m["id"])
- if mid != anchor_id:
- pairs.append((anchor_id, mid))
- elif mode == "mst":
- pairs = mst_edges_for_link(markers)
- else:
- raise ValueError(f"Unknown rigid_distance_mode='{mode}'. Use mst|star|full.")
-
- pos_map = {int(m["id"]): np.asarray(m["position_m"], dtype=np.float64) for m in markers}
- seen_pairs: set[Tuple[int, int]] = set()
-
- for mid_a, mid_b in pairs:
- if mid_a == mid_b:
- continue
- key = tuple(sorted((int(mid_a), int(mid_b))))
- if key in seen_pairs:
- continue
- seen_pairs.add(key)
-
- pos_a = pos_map[mid_a]
- pos_b = pos_map[mid_b]
- target = float(np.linalg.norm(pos_b - pos_a))
-
- constraints.append(
- MarkerDistanceConstraint(
- marker_id_a=mid_a,
- marker_id_b=mid_b,
- link_name=link_name,
- target_distance_m=target,
- weight=cfg.rigid_distance_weight,
- enabled=True,
- source=f"rigid_distance:{mode}",
- )
- )
-
- return constraints
-
-
-def compile_direct_joint_axis_constraints(
- robot_data: Dict[str, Any],
- link_markers: Dict[str, List[Dict[str, Any]]],
- cfg: ConstraintRuleConfig
-) -> List[JointAxisConstraint]:
- constraints: List[JointAxisConstraint] = []
- links = robot_data.get("links", {}) or {}
-
- for child_link, child_data in links.items():
- parent_link = child_data.get("parent", None)
- if not parent_link:
- continue
-
- if not get_enabled_link_rule(robot_data, child_link, "joint_axis_projection", cfg.joint_axis_enabled):
- continue
-
- joint_axis = get_joint_axis(robot_data, child_link)
- if joint_axis is None:
- continue
-
- axis_vec = principal_axis_vector(joint_axis)
-
- parent_markers = link_markers.get(parent_link, [])
- child_markers = link_markers.get(child_link, [])
- if len(parent_markers) == 0 or len(child_markers) == 0:
- continue
-
- max_pairs = max(1, int(cfg.joint_axis_max_pairs))
- parent_anchor_ids = select_anchor_marker_ids(parent_markers, axis=axis_vec, max_count=max_pairs)
- child_anchor_ids = select_anchor_marker_ids(child_markers, axis=axis_vec, max_count=max_pairs)
-
- parent_pos = {int(m["id"]): np.asarray(m["position_m"], dtype=np.float64) for m in parent_markers}
- child_pos = {int(m["id"]): np.asarray(m["position_m"], dtype=np.float64) for m in child_markers}
-
- seen: set[Tuple[int, int]] = set()
- for mid_p in parent_anchor_ids:
- for mid_c in child_anchor_ids:
- if mid_p == mid_c:
- continue
- key = (mid_p, mid_c)
- if key in seen:
- continue
- seen.add(key)
-
- delta = child_pos[mid_c] - parent_pos[mid_p]
- target = float(np.dot(delta, axis_vec))
-
- constraints.append(
- JointAxisConstraint(
- marker_id_parent=mid_p,
- marker_id_child=mid_c,
- parent_link=parent_link,
- child_link=child_link,
- joint_axis=axis_vec,
- target_delta_along_axis_m=target,
- weight=cfg.joint_axis_weight,
- enabled=True,
- source="joint_axis_projection",
- )
- )
-
- return constraints
-
-
-def ancestors_of(link_name: str, parent_map: Dict[str, Optional[str]], max_depth: int) -> List[str]:
- out = []
- cur = parent_map.get(link_name, None)
- depth = 0
- while cur is not None and depth < max_depth:
- out.append(cur)
- cur = parent_map.get(cur, None)
- depth += 1
- return out
-
-
-def path_axes_consistent(
- robot_data: Dict[str, Any],
- ancestor: str,
- descendant: str,
- parent_map: Dict[str, Optional[str]],
- threshold: float
-) -> Optional[np.ndarray]:
- chain: List[str] = []
- cur = descendant
- while cur is not None and cur != ancestor:
- chain.append(cur)
- cur = parent_map.get(cur, None)
-
- if cur != ancestor:
- return None
-
- chain.reverse()
- axes: List[np.ndarray] = []
- for link in chain:
- ax = get_joint_axis(robot_data, link)
- if ax is None:
- return None
- axes.append(ax)
-
- if not axes:
- return None
-
- axis_ids: List[int] = []
- signs: List[float] = []
- for ax in axes:
- aid = principal_axis_id(ax, threshold=threshold)
- if aid is None:
- return None
- axis_ids.append(aid)
- signs.append(float(np.sign(ax[aid])) if abs(ax[aid]) > 1e-12 else 1.0)
-
- if len(set(axis_ids)) != 1:
- return None
-
- axis_id = axis_ids[0]
- sign = 1.0 if np.sum(signs) >= 0 else -1.0
- axis_vec = np.zeros(3, dtype=np.float64)
- axis_vec[axis_id] = sign
- return normalize_vector(axis_vec)
-
-
-def compile_chain_axis_constraints(
- robot_data: Dict[str, Any],
- link_markers: Dict[str, List[Dict[str, Any]]],
- cfg: ConstraintRuleConfig
-) -> List[JointAxisConstraint]:
- constraints: List[JointAxisConstraint] = []
- parent_map = get_link_parent_map(robot_data)
-
- links_with_markers = [ln for ln, mk in link_markers.items() if len(mk) > 0]
- max_depth = max(1, int(cfg.chain_axis_max_depth))
- max_pairs = max(1, int(cfg.chain_axis_max_pairs))
-
- pos_cache: Dict[int, np.ndarray] = {}
- for mk in link_markers.values():
- for m in mk:
- pos_cache[int(m["id"])] = np.asarray(m["position_m"], dtype=np.float64)
-
- for descendant in links_with_markers:
- if not get_enabled_link_rule(robot_data, descendant, "chain_axis_projection", cfg.chain_axis_enabled):
- continue
-
- for ancestor in ancestors_of(descendant, parent_map, max_depth=max_depth):
- if ancestor == descendant:
- continue
-
- axis_vec = path_axes_consistent(
- robot_data=robot_data,
- ancestor=ancestor,
- descendant=descendant,
- parent_map=parent_map,
- threshold=cfg.axis_alignment_threshold,
- )
- if axis_vec is None:
- continue
-
- ancestor_markers = link_markers.get(ancestor, [])
- descendant_markers = link_markers.get(descendant, [])
- if len(ancestor_markers) == 0 or len(descendant_markers) == 0:
- continue
-
- anc_anchors = select_anchor_marker_ids(ancestor_markers, axis=axis_vec, max_count=max_pairs)
- des_anchors = select_anchor_marker_ids(descendant_markers, axis=axis_vec, max_count=max_pairs)
-
- seen: set[Tuple[int, int]] = set()
- for mid_a in anc_anchors:
- for mid_b in des_anchors:
- if mid_a == mid_b:
- continue
- key = (mid_a, mid_b)
- if key in seen:
- continue
- seen.add(key)
-
- target = float(np.dot(pos_cache[mid_b] - pos_cache[mid_a], axis_vec))
- constraints.append(
- JointAxisConstraint(
- marker_id_parent=mid_a,
- marker_id_child=mid_b,
- parent_link=ancestor,
- child_link=descendant,
- joint_axis=axis_vec,
- target_delta_along_axis_m=target,
- weight=cfg.chain_axis_weight,
- enabled=True,
- source=f"chain_axis_projection:depth{len(ancestors_of(descendant, parent_map, max_depth))}",
- )
- )
-
- return constraints
-
-
-def compile_constraints(
- robot_data: Dict[str, Any],
- length_scale: float,
- cfg: ConstraintRuleConfig
-) -> Tuple[Dict[int, str], Dict[str, List[Dict[str, Any]]], List[Constraint], List[str], Dict[int, Dict[str, Any]]]:
- marker_to_link, link_markers, issues, marker_meta = parse_robot_markers(
- robot_data,
- length_scale=length_scale,
- strict_unique_marker_ids=cfg.strict_unique_marker_ids,
- )
-
- constraints: List[Constraint] = []
- constraints.extend(compile_rigid_distance_constraints(robot_data, link_markers, cfg))
- constraints.extend(compile_direct_joint_axis_constraints(robot_data, link_markers, cfg))
- constraints.extend(compile_chain_axis_constraints(robot_data, link_markers, cfg))
-
- unique_constraints: List[Constraint] = []
- seen_keys: set[Tuple[Any, ...]] = set()
-
- for c in constraints:
- if isinstance(c, MarkerDistanceConstraint):
- key = (
- "d",
- min(c.marker_id_a, c.marker_id_b),
- max(c.marker_id_a, c.marker_id_b),
- c.link_name,
- round(c.target_distance_m, 9),
- )
- else:
- key = (
- "j",
- c.parent_link,
- c.child_link,
- c.marker_id_parent,
- c.marker_id_child,
- tuple(np.round(c.joint_axis, 9).tolist()),
- round(c.target_delta_along_axis_m, 9),
- )
- if key in seen_keys:
- continue
- seen_keys.add(key)
- unique_constraints.append(c)
-
- return marker_to_link, link_markers, unique_constraints, issues, marker_meta
-
-
-# ===================================================================
-# Observation quality / weighting
-# ===================================================================
-
-def _optional_float(meta: Dict[str, Any], keys: List[str]) -> Optional[float]:
- for k in keys:
- if k in meta and meta[k] is not None:
- try:
- return float(meta[k])
- except Exception:
- pass
- return None
-
-
-def detection_quality_from_metadata(det_obj: Dict[str, Any], cfg: ConstraintRuleConfig) -> float:
- q = 1.0
-
- if cfg.use_detection_confidence:
- conf = _optional_float(det_obj, ["confidence", "score", "quality", "det_confidence"])
- if conf is not None:
- q *= clamp(conf, 0.1, 1.0)
-
- if cfg.use_detection_size_px:
- size_px = _optional_float(det_obj, ["size_px", "marker_size_px", "side_px", "side_length_px"])
- if size_px is None and "corners_px" in det_obj and isinstance(det_obj["corners_px"], list):
- try:
- corners = np.asarray(det_obj["corners_px"], dtype=np.float64).reshape(-1, 2)
- if len(corners) >= 4:
- edges = []
- for i in range(len(corners)):
- p = corners[i]
- q2 = corners[(i + 1) % len(corners)]
- edges.append(float(np.linalg.norm(q2 - p)))
- size_px = float(np.mean(edges))
- except Exception:
- size_px = None
- if size_px is not None:
- q *= clamp(size_px / max(cfg.ref_marker_size_px, 1e-6), 0.25, 3.0)
-
- sharpness = _optional_float(det_obj, ["sharpness", "corner_sharpness"])
- if sharpness is not None:
- q *= clamp(sharpness / 2500.0, 0.5, 1.5)
-
- normal_alignment = _optional_float(det_obj, ["normal_alignment", "view_cosine", "cos_to_camera"])
- if normal_alignment is not None:
- q *= clamp(normal_alignment, 0.3, 1.0)
-
- return float(q)
-
-
-def marker_size_prior_factor(marker_meta: Dict[str, Any], default_marker_size_m: float) -> float:
- size_val = marker_meta.get("size", None)
- if size_val is None:
- return 1.0
-
- try:
- size_val = float(size_val)
- except Exception:
- return 1.0
-
- size_m = size_val / 1000.0 if size_val > 1.0 else size_val
- ref = max(default_marker_size_m, 1e-6)
- return clamp(size_m / ref, 0.7, 1.3)
-
-
-def compute_observation_weights(
- marker_observations: Dict[int, List[Observation]],
- cameras: List[Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]],
- initial_positions: Dict[int, np.ndarray],
- marker_meta: Dict[int, Dict[str, Any]],
- cfg: ConstraintRuleConfig,
- robot_data: Dict[str, Any]
-) -> Dict[Tuple[int, int], float]:
- weights: Dict[Tuple[int, int], float] = {}
- default_marker_size_m = get_vision_marker_size_default(robot_data)
-
- for marker_id, obs_list in marker_observations.items():
- X = initial_positions.get(marker_id, None)
- size_prior = marker_size_prior_factor(marker_meta.get(marker_id, {}), default_marker_size_m)
-
- for obs_idx, obs in enumerate(obs_list):
- w = 1.0
- q = detection_quality_from_metadata(obs.meta, cfg)
- w *= q
-
- if cfg.use_marker_size_prior:
- w *= size_prior
-
- if cfg.use_initial_range and X is not None:
- _, _, R_wc, t_wc = cameras[obs.cam_idx]
- C = camera_center_from_world_to_cam(R_wc, t_wc)
- dist = float(np.linalg.norm(X - C))
- if np.isfinite(dist):
- w *= clamp(cfg.ref_distance_m / max(dist, 1e-6), 0.4, 2.0)
-
- weights[(marker_id, obs_idx)] = clamp(w, cfg.weight_floor, cfg.weight_ceiling)
-
- return weights
-
-
-# ===================================================================
-# Multi-view loading
-# ===================================================================
-
-def load_observations_and_poses(
- detection_files: List[str],
- pose_files: List[str]
-) -> Tuple[
- Dict[int, List[Observation]],
- List[Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]],
- List[Dict[str, Any]]
-]:
- if len(detection_files) != len(pose_files):
- raise ValueError(f"Mismatch: {len(detection_files)} detections vs {len(pose_files)} poses")
-
- marker_observations: Dict[int, List[Observation]] = {}
- cameras: List[Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]] = []
- obs_metadata: List[Dict[str, Any]] = []
-
- for cam_idx, (det_file, pose_file) in enumerate(zip(detection_files, pose_files)):
- det = load_json(det_file)
- pose_data = load_json(pose_file)
-
- cam_section = det.get("camera", {}) or {}
- K = np.array(cam_section.get("camera_matrix", []), dtype=np.float64).reshape(3, 3)
- D = np.array(cam_section.get("distortion_coefficients", []), dtype=np.float64).reshape(-1, 1)
-
- pose_section = pose_data.get("camera_pose", {}) or {}
- world_to_cam = pose_section.get("world_to_camera", {}) or {}
- R_wc = np.array(world_to_cam.get("rotation_matrix", []), dtype=np.float64).reshape(3, 3)
- t_wc = np.array(world_to_cam.get("translation_m", []), dtype=np.float64).reshape(3)
-
- cameras.append((K, D, R_wc, t_wc))
-
- detections = det.get("detections", []) or []
- for det_obj in detections:
- marker_id = int(det_obj.get("marker_id", -1))
- if marker_id < 0:
- continue
-
- center_px = np.array(det_obj.get("center_px", []), dtype=np.float64)
- if center_px.shape != (2,):
- continue
-
- pts = center_px.reshape(1, 1, 2).astype(np.float32)
- und = cv2.undistortPoints(pts, K.astype(np.float32), D.astype(np.float32), P=None)
- norm_coords = und.reshape(2).astype(np.float64)
-
- obs = Observation(cam_idx=cam_idx, norm_coords=norm_coords, meta=dict(det_obj))
- marker_observations.setdefault(marker_id, []).append(obs)
-
- obs_metadata.append(
- {
- "detection_file": det_file,
- "pose_file": pose_file,
- "num_detections": len(detections),
- }
- )
-
- return marker_observations, cameras, obs_metadata
-
-
-# ===================================================================
-# Initial triangulation
-# ===================================================================
-
-def triangulate_marker_initial(
- marker_id: int,
- observations: List[Observation],
- cameras: List[Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]]
-) -> Optional[np.ndarray]:
- if len(observations) < 2:
- return None
-
- best_pair = None
- best_baseline = -1.0
-
- for obs_i, obs_j in combinations(observations, 2):
- cam_i, cam_j = obs_i.cam_idx, obs_j.cam_idx
- _, _, R1, t1 = cameras[cam_i]
- _, _, R2, t2 = cameras[cam_j]
- c1 = camera_center_from_world_to_cam(R1, t1)
- c2 = camera_center_from_world_to_cam(R2, t2)
- baseline = float(np.linalg.norm(c2 - c1))
- if baseline > best_baseline:
- best_baseline = baseline
- best_pair = (obs_i, obs_j)
-
- if best_pair is None:
- return None
-
- obs_i, obs_j = best_pair
- cam_i, cam_j = obs_i.cam_idx, obs_j.cam_idx
- norm_coords_i = obs_i.norm_coords
- norm_coords_j = obs_j.norm_coords
-
- K1, D1, R1, t1 = cameras[cam_i]
- K2, D2, R2, t2 = cameras[cam_j]
-
- x1_px = K1[0, 0] * norm_coords_i[0] + K1[0, 2]
- y1_px = K1[1, 1] * norm_coords_i[1] + K1[1, 2]
- x2_px = K2[0, 0] * norm_coords_j[0] + K2[0, 2]
- y2_px = K2[1, 1] * norm_coords_j[1] + K2[1, 2]
-
- P1 = K1 @ np.hstack([R1, t1.reshape(3, 1)])
- P2 = K2 @ np.hstack([R2, t2.reshape(3, 1)])
-
- try:
- X_h = cv2.triangulatePoints(
- P1,
- P2,
- np.array([[x1_px], [y1_px]], dtype=np.float64),
- np.array([[x2_px], [y2_px]], dtype=np.float64),
- )
- X = (X_h[:3] / X_h[3]).reshape(3).astype(np.float64)
- if not np.all(np.isfinite(X)):
- return None
- return X
- except Exception:
- return None
-
-
-def initial_triangulation(
- marker_observations: Dict[int, List[Observation]],
- cameras: List[Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]]
-) -> Dict[int, np.ndarray]:
- triangulated: Dict[int, np.ndarray] = {}
- for marker_id, observations in marker_observations.items():
- X = triangulate_marker_initial(marker_id, observations, cameras)
- if X is not None:
- triangulated[marker_id] = X
- return triangulated
-
-
-# ===================================================================
-# Weighted residuals / optimization
-# ===================================================================
-
-def bundle_adjustment_residuals(
- marker_positions_flat: np.ndarray,
- marker_ids: List[int],
- marker_observations: Dict[int, List[Observation]],
- cameras: List[Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]],
- constraints: List[Constraint],
- obs_weights: Dict[Tuple[int, int], float],
- lambda_constraint: float = 100.0
-) -> np.ndarray:
- marker_dict: Dict[int, np.ndarray] = {}
- for i, marker_id in enumerate(marker_ids):
- marker_dict[marker_id] = marker_positions_flat[i * 3:(i + 1) * 3]
-
- residuals: List[float] = []
-
- for marker_id, observations in marker_observations.items():
- if marker_id not in marker_dict:
- continue
-
- X_world = marker_dict[marker_id]
- for obs_idx, obs in enumerate(observations):
- cam_idx, norm_coords_obs = obs.cam_idx, obs.norm_coords
- K, D, R_wc, t_wc = cameras[cam_idx]
- X_cam = R_wc @ X_world + t_wc
- if X_cam[2] > 1e-6:
- proj_norm = X_cam[:2] / X_cam[2]
- r = proj_norm - norm_coords_obs
- w = float(np.sqrt(obs_weights.get((marker_id, obs_idx), 1.0)))
- residuals.append(w * float(r[0]))
- residuals.append(w * float(r[1]))
-
- for constraint in constraints:
- if isinstance(constraint, MarkerDistanceConstraint):
- if constraint.marker_id_a in marker_dict and constraint.marker_id_b in marker_dict:
- pos_a = marker_dict[constraint.marker_id_a]
- pos_b = marker_dict[constraint.marker_id_b]
- actual_dist = float(np.linalg.norm(pos_b - pos_a))
- residuals.append((actual_dist - constraint.target_distance_m) * constraint.weight * lambda_constraint)
-
- elif isinstance(constraint, JointAxisConstraint):
- if constraint.marker_id_parent in marker_dict and constraint.marker_id_child in marker_dict:
- pos_parent = marker_dict[constraint.marker_id_parent]
- pos_child = marker_dict[constraint.marker_id_child]
- delta = pos_child - pos_parent
- actual_delta = float(np.dot(delta, constraint.joint_axis))
- residuals.append((actual_delta - constraint.target_delta_along_axis_m) * constraint.weight * lambda_constraint)
-
- return np.asarray(residuals, dtype=np.float64)
-
-
-def optimize_with_constraints(
- initial_positions: Dict[int, np.ndarray],
- marker_observations: Dict[int, List[Observation]],
- cameras: List[Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]],
- constraints: List[Constraint],
- obs_weights: Dict[Tuple[int, int], float],
- lambda_constraint: float = 100.0,
- max_iterations: int = 50
-) -> Dict[int, np.ndarray]:
- try:
- from scipy.optimize import least_squares
- except ImportError:
- print("[WARN] scipy not available, skipping optimization.")
- return initial_positions
-
- marker_ids = sorted(initial_positions.keys())
- if not marker_ids:
- return {}
-
- x0 = np.concatenate([initial_positions[mid] for mid in marker_ids])
-
- def residuals_fn(x: np.ndarray) -> np.ndarray:
- return bundle_adjustment_residuals(
- x, marker_ids, marker_observations, cameras, constraints, obs_weights, lambda_constraint
- )
-
- print(f"[INFO] Starting optimization with {len(x0)} variables and {len(constraints)} constraints...")
-
- result = least_squares(
- residuals_fn,
- x0,
- max_nfev=max_iterations * max(1, len(marker_ids)),
- verbose=1,
- )
-
- optimized = {}
- for i, marker_id in enumerate(marker_ids):
- optimized[marker_id] = result.x[i * 3:(i + 1) * 3]
-
- print(f"[INFO] Optimization complete. Final cost: {float(np.sum(result.fun ** 2)):.6f}")
- return optimized
-
-
-# ===================================================================
-# Reporting helpers
-# ===================================================================
-
-def print_constraint_summary(constraints: List[Constraint]) -> None:
- num_dist = sum(isinstance(c, MarkerDistanceConstraint) for c in constraints)
- num_joint = sum(isinstance(c, JointAxisConstraint) for c in constraints)
- print(f"[INFO] Constraint summary: total={len(constraints)} distance={num_dist} joint/chain={num_joint}")
-
-
-def print_constraint_list(constraints: List[Constraint]) -> None:
- print("\n[INFO] Constraint list:")
- for idx, constraint in enumerate(constraints):
- if isinstance(constraint, MarkerDistanceConstraint):
- print(
- f" [{idx:03d}] DISTANCE | "
- f"Link='{constraint.link_name}' | "
- f"M{constraint.marker_id_a} <-> M{constraint.marker_id_b} | "
- f"Target={constraint.target_distance_m:.6f} m | "
- f"Weight={constraint.weight} | "
- f"Source={constraint.source}"
- )
- else:
- axis_str = np.array2string(constraint.joint_axis, precision=3, suppress_small=True)
- print(
- f" [{idx:03d}] JOINT_AXIS | "
- f"{constraint.parent_link}(M{constraint.marker_id_parent}) -> "
- f"{constraint.child_link}(M{constraint.marker_id_child}) | "
- f"Axis={axis_str} | "
- f"TargetDelta={constraint.target_delta_along_axis_m:.6f} m | "
- f"Weight={constraint.weight} | "
- f"Source={constraint.source}"
- )
-
-
-def print_constraints_with_errors(
- title: str,
- constraints: List[Constraint],
- positions: Dict[int, np.ndarray],
- show_skipped: bool = True
-) -> None:
- print(f"\n[INFO] {title}")
-
- active = 0
- skipped = 0
-
- for idx, constraint in enumerate(constraints):
- if isinstance(constraint, MarkerDistanceConstraint):
- if constraint.marker_id_a not in positions or constraint.marker_id_b not in positions:
- skipped += 1
- if show_skipped:
- print(
- f" [{idx:03d}] DISTANCE | "
- f"M{constraint.marker_id_a} <-> M{constraint.marker_id_b} | SKIPPED (missing marker)"
- )
- continue
-
- pos_a = positions[constraint.marker_id_a]
- pos_b = positions[constraint.marker_id_b]
- actual = float(np.linalg.norm(pos_b - pos_a))
- error = actual - constraint.target_distance_m
- active += 1
-
- print(
- f" [{idx:03d}] DISTANCE | "
- f"Link='{constraint.link_name}' | "
- f"M{constraint.marker_id_a} <-> M{constraint.marker_id_b} | "
- f"target={constraint.target_distance_m*1000:.2f} mm | "
- f"actual={actual*1000:.2f} mm | "
- f"error={error*1000:+.2f} mm"
- )
-
- elif isinstance(constraint, JointAxisConstraint):
- if constraint.marker_id_parent not in positions or constraint.marker_id_child not in positions:
- skipped += 1
- if show_skipped:
- print(
- f" [{idx:03d}] JOINT_AXIS | "
- f"M{constraint.marker_id_parent} -> M{constraint.marker_id_child} | SKIPPED (missing marker)"
- )
- continue
-
- pos_parent = positions[constraint.marker_id_parent]
- pos_child = positions[constraint.marker_id_child]
- delta = pos_child - pos_parent
- actual = float(np.dot(delta, constraint.joint_axis))
- error = actual - constraint.target_delta_along_axis_m
- active += 1
-
- axis_str = np.array2string(constraint.joint_axis, precision=2, suppress_small=True)
- print(
- f" [{idx:03d}] JOINT_AXIS | "
- f"{constraint.parent_link}(M{constraint.marker_id_parent}) -> "
- f"{constraint.child_link}(M{constraint.marker_id_child}) | "
- f"axis={axis_str} | "
- f"target={constraint.target_delta_along_axis_m*1000:.2f} mm | "
- f"actual={actual*1000:.2f} mm | "
- f"error={error*1000:+.2f} mm"
- )
-
- print(f"[INFO] Active constraints: {active} | Skipped: {skipped}")
-
-
-def print_observation_weight_summary(obs_weights: Dict[Tuple[int, int], float]) -> None:
- if not obs_weights:
- print("[INFO] Observation weighting: disabled or empty")
- return
- values = np.array(list(obs_weights.values()), dtype=np.float64)
- print(
- "[INFO] Observation weights: "
- f"min={values.min():.3f} mean={values.mean():.3f} "
- f"median={np.median(values):.3f} max={values.max():.3f}"
- )
-
-
-# ===================================================================
-# Main
-# ===================================================================
-
-def main() -> None:
- parser = argparse.ArgumentParser(
- description="Multi-view bundle adjustment with rule-based geometric constraints"
- )
- parser.add_argument(
- "-det", "--detections",
- action="append",
- required=True,
- help="*_aruco_detection.json files"
- )
- parser.add_argument(
- "-pose", "--poses",
- action="append",
- required=True,
- help="*_camera_pose.json files"
- )
- parser.add_argument(
- "-robot", "--robot",
- required=True,
- help="robot.json"
- )
- parser.add_argument(
- "-outDir", "--outDir",
- default=None,
- help="Output directory"
- )
- parser.add_argument(
- "-lambdaWeight", "--lambdaWeight",
- type=float,
- default=100.0,
- help="Constraint weight multiplier"
- )
- parser.add_argument(
- "--strictUniqueMarkerIds",
- action="store_true",
- help="Fail if a marker ID appears more than once in robot.json"
- )
- parser.add_argument(
- "--showSkippedConstraints",
- action="store_true",
- help="Print skipped constraints in the report"
- )
- parser.add_argument(
- "--noShowSkippedConstraints",
- action="store_true",
- help="Hide skipped constraints in the report"
- )
- parser.add_argument(
- "--saveConstraintReport",
- action="store_true",
- help="Save constraint report JSON files"
- )
- parser.add_argument(
- "--saveObservationWeightReport",
- action="store_true",
- help="Save observation-weight report JSON file"
- )
-
- args = parser.parse_args()
-
- if args.showSkippedConstraints and args.noShowSkippedConstraints:
- print("[ERROR] Choose only one of --showSkippedConstraints or --noShowSkippedConstraints")
- sys.exit(1)
-
- if len(args.detections) != len(args.poses):
- print(f"[ERROR] Mismatch: {len(args.detections)} detection files vs {len(args.poses)} pose files")
- sys.exit(1)
-
- robot_data = load_json(args.robot)
- length_scale = get_length_scale(robot_data)
- cfg = load_constraint_rule_config(robot_data, args)
-
- print("[STEP 1] Compile constraints from robot.json structure...")
- marker_to_link, link_markers, constraints, issues, marker_meta = compile_constraints(robot_data, length_scale, cfg)
-
- for issue in issues:
- print(issue)
-
- print(f"[INFO] Links with markers: {sum(1 for v in link_markers.values() if len(v) > 0)}")
- print(f"[INFO] Unique marker IDs: {len(marker_to_link)}")
- print_constraint_summary(constraints)
- print_constraint_list(constraints)
-
- print("\n[STEP 2] Load observations and camera poses...")
- marker_observations, cameras, obs_metadata = load_observations_and_poses(args.detections, args.poses)
- print(f"[INFO] {len(cameras)} cameras, {len(marker_observations)} observed markers")
- print(f"[INFO] Detection files loaded: {len(obs_metadata)}")
-
- print("\n[STEP 3] Initial triangulation...")
- initial_pos = initial_triangulation(marker_observations, cameras)
- print(f"[INFO] Triangulated {len(initial_pos)} markers")
-
- out_dir = args.outDir or os.path.dirname(args.detections[0]) or "."
- os.makedirs(resolve_path(out_dir), exist_ok=True)
-
- initial_output_markers = []
- for marker_id, position in sorted(initial_pos.items()):
- initial_output_markers.append(
- {
- "marker_id": int(marker_id),
- "position_m": [float(x) for x in position],
- "position_mm": [float(x * 1000.0) for x in position],
- "link": marker_to_link.get(marker_id, "unknown"),
- }
- )
-
- initial_output = {
- "schema_version": "1.2",
- "stage": "initial_triangulation",
- "created_utc": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
- "summary": {
- "num_cameras": len(cameras),
- "num_markers": len(initial_pos),
- "num_constraints": len(constraints),
- },
- "markers": initial_output_markers,
- }
- initial_out_file = os.path.join(out_dir, "aruco_positions_initial.json")
- save_json(initial_out_file, initial_output)
- print(f"[INFO] Initial triangulation saved to {initial_out_file}")
-
- obs_weights = compute_observation_weights(
- marker_observations=marker_observations,
- cameras=cameras,
- initial_positions=initial_pos,
- marker_meta=marker_meta,
- cfg=cfg,
- robot_data=robot_data,
- )
- print_observation_weight_summary(obs_weights)
-
- print_constraints_with_errors(
- "Constraint list BEFORE optimization",
- constraints,
- initial_pos,
- show_skipped=cfg.show_skipped_constraints,
- )
-
- print("\n[STEP 4] Bundle adjustment with constraints...")
- optimized_pos = optimize_with_constraints(
- initial_pos,
- marker_observations,
- cameras,
- constraints,
- obs_weights,
- lambda_constraint=args.lambdaWeight,
- )
-
- print_constraints_with_errors(
- "Constraint list AFTER optimization",
- constraints,
- optimized_pos,
- show_skipped=cfg.show_skipped_constraints,
- )
-
- output_markers = []
- for marker_id, position in sorted(optimized_pos.items()):
- output_markers.append(
- {
- "marker_id": int(marker_id),
- "position_m": [float(x) for x in position],
- "position_mm": [float(x * 1000.0) for x in position],
- "link": marker_to_link.get(marker_id, "unknown"),
- }
- )
-
- output = {
- "schema_version": "1.2",
- "created_utc": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
- "summary": {
- "num_cameras": len(cameras),
- "num_markers": len(optimized_pos),
- "num_constraints": len(constraints),
- },
- "markers": output_markers,
- }
- out_file = os.path.join(out_dir, "aruco_positions_optimized.json")
- save_json(out_file, output)
- print(f"\n[INFO] Saved to {out_file}")
-
- if args.saveConstraintReport:
- report = {
- "schema_version": "1.0",
- "created_utc": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
- "summary": {
- "num_constraints": len(constraints),
- "num_links_with_markers": sum(1 for v in link_markers.values() if len(v) > 0),
- "num_observed_markers": len(marker_observations),
- "num_triangulated_markers": len(initial_pos),
- "num_optimized_markers": len(optimized_pos),
- },
- "constraints": [],
- }
- for c in constraints:
- if isinstance(c, MarkerDistanceConstraint):
- report["constraints"].append(
- {
- "kind": "distance",
- "link_name": c.link_name,
- "marker_id_a": c.marker_id_a,
- "marker_id_b": c.marker_id_b,
- "target_distance_m": c.target_distance_m,
- "weight": c.weight,
- "source": c.source,
- }
- )
- else:
- report["constraints"].append(
- {
- "kind": "joint_axis",
- "parent_link": c.parent_link,
- "child_link": c.child_link,
- "marker_id_parent": c.marker_id_parent,
- "marker_id_child": c.marker_id_child,
- "joint_axis": [float(x) for x in c.joint_axis],
- "target_delta_along_axis_m": c.target_delta_along_axis_m,
- "weight": c.weight,
- "source": c.source,
- }
- )
- report_file = os.path.join(out_dir, "constraint_report.json")
- save_json(report_file, report)
- print(f"[INFO] Constraint report saved to {report_file}")
-
- if args.saveObservationWeightReport:
- obs_report = {
- "schema_version": "1.0",
- "created_utc": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
- "summary": {
- "num_weighted_observations": len(obs_weights),
- },
- "observation_weights": [
- {
- "marker_id": int(mid),
- "observation_index": int(obs_idx),
- "weight": float(w),
- }
- for (mid, obs_idx), w in sorted(obs_weights.items())
- ],
- }
- obs_file = os.path.join(out_dir, "observation_weight_report.json")
- save_json(obs_file, obs_report)
- print(f"[INFO] Observation-weight report saved to {obs_file}")
-
-
-if __name__ == "__main__":
- main()
diff --git a/pipeline/4_robotState_estimation_v4_seq.py b/pipeline/4_robotState_estimation_v4_seq.py
deleted file mode 100644
index d72dcf5..0000000
--- a/pipeline/4_robotState_estimation_v4_seq.py
+++ /dev/null
@@ -1,1182 +0,0 @@
-#!/usr/bin/env python3
-"""
-4_robotState_estimation_v2.py
-
-Sequential robot-state estimation from optimized 3D ArUco marker positions.
-
-Mathematical idea
------------------
-This script estimates a robot state q from already optimized marker positions p_i^obs.
-
-The robot state consists of:
- - a world pose for the root link (translation + rotation)
- - one scalar variable for each actuated joint
-
-Forward kinematics predicts each marker position:
- p_i^pred(q) = T_world(link(i); q) * p_i^local
-
-Instead of solving the whole robot at once, we solve it sequentially by kinematic prefix:
- stage 0: root link
- stage 1: root + first child layer
- stage 2: root + first three connected elements ...
- ...
-Each stage reuses the previous solution as initialization and only activates the
-joint variables that belong to the current kinematic prefix.
-
-The residual minimized at each stage is:
- sum_i w_i ||p_i^pred(q) - p_i^obs||^2
- + weak priors on the active joint values
- + weak prior on the root pose
-
-This is intentionally conservative and debuggable. It is designed so that:
- - early links can be resolved first,
- - later links only refine an already plausible state,
- - one visible marker on an early rigid prefix can already fix a lot of state,
- - ambiguous branches can later be resolved by adding more links.
-
-Optional future extension:
- raw detections + camera poses can be added later as visibility / normal cues,
- but this script intentionally works directly from aruco_positions_optimized*.json.
-
-Dependencies
-------------
-numpy, scipy (optional but strongly recommended)
-"""
-
-from __future__ import annotations
-
-import argparse
-import json
-import math
-import os
-import sys
-import time
-from dataclasses import dataclass
-from typing import Any, Dict, List, Optional, Tuple
-
-import numpy as np
-
-
-# =============================================================================
-# JSON / path helpers
-# =============================================================================
-
-def resolve_path(path: str) -> str:
- path = os.path.expanduser(path)
- if os.path.isabs(path):
- return path
- return os.path.abspath(path)
-
-
-def load_json(path: str) -> Any:
- with open(resolve_path(path), "r", encoding="utf-8") as f:
- return json.load(f)
-
-
-def save_json(path: str, data: Any) -> None:
- with open(resolve_path(path), "w", encoding="utf-8") as f:
- json.dump(data, f, indent=2)
-
-
-# =============================================================================
-# Small math helpers
-# =============================================================================
-
-def safe_norm(v: np.ndarray, eps: float = 1e-12) -> float:
- return float(np.linalg.norm(v) + eps)
-
-
-def normalize(v: np.ndarray, eps: float = 1e-12) -> np.ndarray:
- v = np.asarray(v, dtype=np.float64)
- return v / safe_norm(v, eps)
-
-
-def get_length_scale(robot_data: Dict[str, Any]) -> float:
- units = robot_data.get("units", {}) or {}
- length_unit = str(units.get("length", "")).strip().lower()
- if length_unit in ("mm", "millimeter", "millimeters"):
- return 1.0 / 1000.0
- if length_unit in ("cm", "centimeter", "centimeters"):
- return 1.0 / 100.0
- return 1.0
-
-
-def rotation_matrix_xyz(rx: float, ry: float, rz: float, degrees: bool = False) -> np.ndarray:
- """Rotation order: X then Y then Z."""
- if degrees:
- rx, ry, rz = math.radians(rx), math.radians(ry), math.radians(rz)
-
- cx, sx = math.cos(rx), math.sin(rx)
- cy, sy = math.cos(ry), math.sin(ry)
- cz, sz = math.cos(rz), math.sin(rz)
-
- rx_m = np.array([[1.0, 0.0, 0.0],
- [0.0, cx, -sx],
- [0.0, sx, cx]], dtype=np.float64)
- ry_m = np.array([[cy, 0.0, sy],
- [0.0, 1.0, 0.0],
- [-sy, 0.0, cy]], dtype=np.float64)
- rz_m = np.array([[cz, -sz, 0.0],
- [sz, cz, 0.0],
- [0.0, 0.0, 1.0]], dtype=np.float64)
- return rz_m @ ry_m @ rx_m
-
-
-def axis_angle_rotation(axis: np.ndarray, angle_rad: float) -> np.ndarray:
- axis = normalize(axis)
- x, y, z = axis
- c = math.cos(angle_rad)
- s = math.sin(angle_rad)
- C = 1.0 - c
- return np.array([
- [c + x * x * C, x * y * C - z * s, x * z * C + y * s],
- [y * x * C + z * s, c + y * y * C, y * z * C - x * s],
- [z * x * C - y * s, z * y * C + x * s, c + z * z * C]
- ], dtype=np.float64)
-
-
-def make_transform(R: Optional[np.ndarray] = None, t: Optional[np.ndarray] = None) -> np.ndarray:
- T = np.eye(4, dtype=np.float64)
- if R is not None:
- T[:3, :3] = np.asarray(R, dtype=np.float64)
- if t is not None:
- T[:3, 3] = np.asarray(t, dtype=np.float64).reshape(3)
- return T
-
-
-def transform_point(T: np.ndarray, p: np.ndarray) -> np.ndarray:
- p4 = np.ones(4, dtype=np.float64)
- p4[:3] = np.asarray(p, dtype=np.float64).reshape(3)
- return (T @ p4)[:3]
-
-
-def matrix_to_euler_xyz(R: np.ndarray) -> np.ndarray:
- """Return XYZ Euler angles in degrees."""
- R = np.asarray(R, dtype=np.float64)
- sy = math.sqrt(R[0, 0] * R[0, 0] + R[1, 0] * R[1, 0])
- singular = sy < 1e-9
-
- if not singular:
- x = math.atan2(R[2, 1], R[2, 2])
- y = math.atan2(-R[2, 0], sy)
- z = math.atan2(R[1, 0], R[0, 0])
- else:
- x = math.atan2(-R[1, 2], R[1, 1])
- y = math.atan2(-R[2, 0], sy)
- z = 0.0
- return np.degrees([x, y, z])
-
-
-def wrap_angle_rad(a: float) -> float:
- return (a + math.pi) % (2.0 * math.pi) - math.pi
-
-
-def kabsch(P: np.ndarray, Q: np.ndarray, W: Optional[np.ndarray] = None) -> Tuple[np.ndarray, np.ndarray]:
- """Find R, t such that R @ P + t ≈ Q."""
- P = np.asarray(P, dtype=np.float64)
- Q = np.asarray(Q, dtype=np.float64)
- assert P.shape == Q.shape and P.shape[1] == 3
-
- if W is None:
- W = np.ones(len(P), dtype=np.float64)
- W = np.asarray(W, dtype=np.float64).reshape(-1)
- W = np.maximum(W, 1e-12)
- W = W / np.sum(W)
-
- p_cent = np.sum(P * W[:, None], axis=0)
- q_cent = np.sum(Q * W[:, None], axis=0)
-
- P0 = P - p_cent
- Q0 = Q - q_cent
- H = (P0 * W[:, None]).T @ Q0
-
- U, S, Vt = np.linalg.svd(H)
- R = Vt.T @ U.T
- if np.linalg.det(R) < 0:
- Vt[-1, :] *= -1.0
- R = Vt.T @ U.T
- t = q_cent - R @ p_cent
- return R, t
-
-
-# =============================================================================
-# Data structures
-# =============================================================================
-
-@dataclass
-class MarkerInfo:
- marker_id: int
- link_name: str
- local_pos_m: np.ndarray
- size: Optional[float] = None
- normal: Optional[np.ndarray] = None
- name: str = ""
-
-
-@dataclass
-class LinkInfo:
- name: str
- parent: Optional[str]
- joint_type: Optional[str]
- joint_axis: Optional[np.ndarray]
- joint_origin_m: np.ndarray
- joint_rotation_deg: np.ndarray
- joint_variable: Optional[str]
- mount_position_m: np.ndarray
- mount_rotation_deg: np.ndarray
- markers: List[MarkerInfo]
-
-
-@dataclass
-class StageResult:
- depth: int
- active_links: List[str]
- active_joint_vars: List[str]
- mean_error_m: Optional[float]
- rms_error_m: Optional[float]
- worst_error_m: Optional[float]
- num_markers_used: int
- optimizer_info: Dict[str, Any]
-
-
-@dataclass
-class EstimationConfig:
- root_pose_prior_weight: float = 0.1
- joint_prior_weight: float = 0.05
- marker_base_weight: float = 1.0
- robust_loss: str = "soft_l1"
- max_iterations: int = 120
- include_joint_prior: bool = True
- include_root_prior: bool = True
- sequential: bool = True
- show_stage_reports: bool = True
-
-
-# =============================================================================
-# Robot parsing
-# =============================================================================
-
-def parse_robot(robot_data: Dict[str, Any]) -> Tuple[Dict[str, LinkInfo], Dict[int, MarkerInfo], List[str]]:
- length_scale = get_length_scale(robot_data)
- links = robot_data.get("links", {}) or {}
-
- link_infos: Dict[str, LinkInfo] = {}
- marker_by_id: Dict[int, MarkerInfo] = {}
- issues: List[str] = []
-
- for link_name, link_data in links.items():
- parent = link_data.get("parent", None)
-
- joint = link_data.get("jointToParent", {}) or {}
- joint_type = joint.get("type", None)
-
- axis = joint.get("axis", None)
- if axis is not None:
- axis = np.asarray(axis, dtype=np.float64)
- if safe_norm(axis) > 1e-12:
- axis = normalize(axis)
- else:
- axis = None
-
- joint_origin = np.asarray(joint.get("origin", [0, 0, 0]), dtype=np.float64) * float(length_scale)
- joint_rotation_deg = np.asarray(joint.get("rotation", [0, 0, 0]), dtype=np.float64)
- joint_variable = joint.get("variable", None)
-
- mount_position = np.asarray(link_data.get("mountPosition", [0, 0, 0]), dtype=np.float64) * float(length_scale)
- mount_rotation_deg = np.asarray(link_data.get("mountRotation", [0, 0, 0]), dtype=np.float64)
-
- markers_data = link_data.get("markers", []) or []
- markers: List[MarkerInfo] = []
- for idx, m in enumerate(markers_data):
- marker_id = int(m.get("id", -1))
- pos = m.get("position", None)
-
- if marker_id < 0 or pos is None or len(pos) != 3:
- issues.append(f"[WARN] link='{link_name}': skipped invalid marker entry at index {idx}")
- continue
-
- if marker_id in marker_by_id:
- issues.append(
- f"[WARN] duplicate marker ID {marker_id} in link '{link_name}' "
- f"(already in '{marker_by_id[marker_id].link_name}'); using first occurrence"
- )
- continue
-
- normal = None
- if "normal" in m and isinstance(m["normal"], list) and len(m["normal"]) == 3:
- normal = np.asarray(m["normal"], dtype=np.float64)
-
- marker = MarkerInfo(
- marker_id=marker_id,
- link_name=link_name,
- local_pos_m=np.asarray(pos, dtype=np.float64) * float(length_scale),
- size=m.get("size", None),
- normal=normal,
- name=str(m.get("name", f"marker_{marker_id}")),
- )
- markers.append(marker)
- marker_by_id[marker_id] = marker
-
- link_infos[link_name] = LinkInfo(
- name=link_name,
- parent=parent,
- joint_type=joint_type,
- joint_axis=axis,
- joint_origin_m=joint_origin,
- joint_rotation_deg=joint_rotation_deg,
- joint_variable=joint_variable,
- mount_position_m=mount_position,
- mount_rotation_deg=mount_rotation_deg,
- markers=markers,
- )
-
- return link_infos, marker_by_id, issues
-
-
-def topological_links(link_infos: Dict[str, LinkInfo]) -> List[str]:
- children = {name: [] for name in link_infos}
- roots = []
- for name, info in link_infos.items():
- if info.parent is None:
- roots.append(name)
- else:
- children.setdefault(info.parent, []).append(name)
-
- order: List[str] = []
- queue = list(roots)
- seen = set()
-
- while queue:
- cur = queue.pop(0)
- if cur in seen:
- continue
- seen.add(cur)
- order.append(cur)
- for ch in children.get(cur, []):
- queue.append(ch)
-
- for name in link_infos:
- if name not in seen:
- order.append(name)
- return order
-
-
-def compute_link_depths(link_infos: Dict[str, LinkInfo]) -> Dict[str, int]:
- depths: Dict[str, int] = {}
-
- def depth_of(name: str) -> int:
- if name in depths:
- return depths[name]
- info = link_infos[name]
- if info.parent is None:
- depths[name] = 0
- else:
- depths[name] = depth_of(info.parent) + 1
- return depths[name]
-
- for name in link_infos:
- depth_of(name)
- return depths
-
-
-# =============================================================================
-# Marker weights
-# =============================================================================
-
-def marker_weight(marker_info: MarkerInfo, base_weight: float = 1.0, ref_size: float = 25.0) -> float:
- """
- Weight the marker residuals lightly by marker size.
- Larger markers tend to be more stable in image space.
- """
- w = base_weight
- if marker_info.size is not None:
- try:
- size = float(marker_info.size)
- if size > 0:
- w *= max(0.35, math.sqrt(size / ref_size))
- except Exception:
- pass
- return w
-
-
-# =============================================================================
-# Forward kinematics
-# =============================================================================
-
-def link_static_transform(link: LinkInfo) -> np.ndarray:
- Rm = rotation_matrix_xyz(*link.mount_rotation_deg, degrees=True)
- return make_transform(Rm, link.mount_position_m)
-
-
-def joint_motion_transform(link: LinkInfo, q_value: float) -> np.ndarray:
- joint_type = (link.joint_type or "").strip().lower()
- if link.joint_axis is None:
- return np.eye(4, dtype=np.float64)
-
- axis = normalize(link.joint_axis)
- if joint_type == "linear":
- return make_transform(np.eye(3), axis * float(q_value))
- if joint_type == "revolute":
- return make_transform(axis_angle_rotation(axis, float(q_value)), np.zeros(3))
- return np.eye(4, dtype=np.float64)
-
-
-def world_to_link_transform(
- link_name: str,
- link_infos: Dict[str, LinkInfo],
- state: Dict[str, Any],
- cache: Dict[str, np.ndarray],
-) -> np.ndarray:
- if link_name in cache:
- return cache[link_name]
-
- link = link_infos[link_name]
- if link.parent is None:
- T = make_transform(state["root_R"], state["root_t"])
- cache[link_name] = T
- return T
-
- parent_T = world_to_link_transform(link.parent, link_infos, state, cache)
-
- # Convention:
- # parent frame -> joint origin -> joint motion -> static joint rotation -> child mount transform
- T = parent_T @ make_transform(np.eye(3), link.joint_origin_m)
-
- q = state["joint_values"].get(link.joint_variable, state["joint_defaults"].get(link.joint_variable, 0.0))
- T = T @ joint_motion_transform(link, q)
-
- R_static = rotation_matrix_xyz(*link.joint_rotation_deg, degrees=True)
- T = T @ make_transform(R_static, np.zeros(3))
-
- T = T @ link_static_transform(link)
-
- cache[link_name] = T
- return T
-
-
-def predict_marker_positions(
- link_infos: Dict[str, LinkInfo],
- state: Dict[str, Any],
-) -> Dict[int, np.ndarray]:
- pred: Dict[int, np.ndarray] = {}
- cache: Dict[str, np.ndarray] = {}
- for link_name, link in link_infos.items():
- T = world_to_link_transform(link_name, link_infos, state, cache)
- for marker in link.markers:
- pred[marker.marker_id] = transform_point(T, marker.local_pos_m)
- return pred
-
-
-# =============================================================================
-# Initial parameters
-# =============================================================================
-
-def infer_joint_defaults(robot_data: Dict[str, Any], link_infos: Dict[str, LinkInfo]) -> Dict[str, float]:
- defaults_raw = robot_data.get("defaultPosition", {}) or {}
- out: Dict[str, float] = {}
-
- for link in link_infos.values():
- var = link.joint_variable
- if not var:
- continue
- raw_val = defaults_raw.get(var, 0.0)
- joint_type = (link.joint_type or "").strip().lower()
- if joint_type == "linear":
- out[var] = float(raw_val) * get_length_scale(robot_data)
- elif joint_type == "revolute":
- out[var] = math.radians(float(raw_val))
- else:
- out[var] = float(raw_val)
- return out
-
-
-def initial_root_pose(
- link_infos: Dict[str, LinkInfo],
- observed_markers: Dict[int, Dict[str, Any]],
-) -> Tuple[np.ndarray, np.ndarray]:
- roots = [name for name, info in link_infos.items() if info.parent is None]
- if not roots:
- return np.eye(3, dtype=np.float64), np.zeros(3, dtype=np.float64)
-
- root = roots[0]
- root_info = link_infos[root]
-
- P = []
- Q = []
- W = []
-
- for marker in root_info.markers:
- if marker.marker_id in observed_markers:
- obs = np.asarray(observed_markers[marker.marker_id]["position_m"], dtype=np.float64)
- P.append(marker.local_pos_m)
- Q.append(obs)
- W.append(marker_weight(marker, base_weight=1.0))
-
- if len(P) >= 3:
- return kabsch(np.asarray(P), np.asarray(Q), np.asarray(W))
-
- return np.eye(3, dtype=np.float64), np.zeros(3, dtype=np.float64)
-
-
-def build_initial_state(
- robot_data: Dict[str, Any],
- link_infos: Dict[str, LinkInfo],
- observed_markers: Dict[int, Dict[str, Any]],
-) -> Dict[str, Any]:
- root_R, root_t = initial_root_pose(link_infos, observed_markers)
- joint_defaults = infer_joint_defaults(robot_data, link_infos)
-
- return {
- "root_R": root_R,
- "root_t": root_t,
- "joint_defaults": joint_defaults,
- "joint_values": dict(joint_defaults),
- }
-
-
-# =============================================================================
-# Stage helpers
-# =============================================================================
-
-def active_links_for_depth(link_infos: Dict[str, LinkInfo], depths: Dict[str, int], max_depth: int) -> List[str]:
- ordered = topological_links(link_infos)
- return [name for name in ordered if depths.get(name, 0) <= max_depth]
-
-
-def active_joint_vars_for_links(link_infos: Dict[str, LinkInfo], active_links: List[str]) -> List[str]:
- vars_out: List[str] = []
- seen = set()
- for name in active_links:
- link = link_infos[name]
- if not link.joint_variable:
- continue
- if link.joint_variable in seen:
- continue
- seen.add(link.joint_variable)
- vars_out.append(link.joint_variable)
- return vars_out
-
-
-def active_observations_for_links(
- link_infos: Dict[str, LinkInfo],
- observed_markers: Dict[int, Dict[str, Any]],
- active_links: List[str],
-) -> Dict[int, Dict[str, Any]]:
- allowed_links = set(active_links)
- out: Dict[int, Dict[str, Any]] = {}
- for marker_id, obs in observed_markers.items():
- link_name = obs.get("link", None)
- if link_name in allowed_links:
- out[marker_id] = obs
- return out
-
-
-def stage_variables_to_vector(
- state: Dict[str, Any],
- active_joint_vars: List[str],
-) -> np.ndarray:
- try:
- from scipy.spatial.transform import Rotation
- root_rvec = Rotation.from_matrix(state["root_R"]).as_rotvec()
- except Exception:
- root_rvec = np.zeros(3, dtype=np.float64)
-
- root_t = np.asarray(state["root_t"], dtype=np.float64).reshape(3)
- values = list(root_t) + list(np.asarray(root_rvec, dtype=np.float64))
- for var in active_joint_vars:
- values.append(float(state["joint_values"].get(var, state["joint_defaults"].get(var, 0.0))))
- return np.asarray(values, dtype=np.float64)
-
-
-def vector_to_stage_state(
- x: np.ndarray,
- template_state: Dict[str, Any],
- active_joint_vars: List[str],
-) -> Dict[str, Any]:
- try:
- from scipy.spatial.transform import Rotation
- except Exception:
- Rotation = None
-
- x = np.asarray(x, dtype=np.float64).reshape(-1)
-
- out = {
- "root_t": x[0:3].copy(),
- "root_R": template_state["root_R"].copy(),
- "joint_defaults": dict(template_state["joint_defaults"]),
- "joint_values": dict(template_state["joint_values"]),
- }
-
- root_rvec = x[3:6].copy()
- if Rotation is not None:
- out["root_R"] = Rotation.from_rotvec(root_rvec).as_matrix()
- else:
- angle = safe_norm(root_rvec)
- if angle < 1e-12:
- out["root_R"] = np.eye(3, dtype=np.float64)
- else:
- out["root_R"] = axis_angle_rotation(root_rvec / angle, angle)
-
- idx = 6
- for var in active_joint_vars:
- if idx >= len(x):
- break
- out["joint_values"][var] = float(x[idx])
- idx += 1
-
- return out
-
-
-def residuals_stage(
- x: np.ndarray,
- link_infos: Dict[str, LinkInfo],
- observed_markers: Dict[int, Dict[str, Any]],
- template_state: Dict[str, Any],
- active_joint_vars: List[str],
- cfg: EstimationConfig,
-) -> np.ndarray:
- state = vector_to_stage_state(x, template_state, active_joint_vars)
- pred = predict_marker_positions(link_infos, state)
-
- res: List[float] = []
-
- # Marker residuals
- for marker_id, obs in observed_markers.items():
- if marker_id not in pred:
- continue
-
- link_name = obs.get("link", None)
- marker = None
- if link_name is not None and link_name in link_infos:
- for m in link_infos[link_name].markers:
- if m.marker_id == marker_id:
- marker = m
- break
- if marker is None:
- continue
-
- w = marker_weight(marker, base_weight=cfg.marker_base_weight)
- sqrt_w = math.sqrt(max(1e-12, w))
-
- p_obs = np.asarray(obs["position_m"], dtype=np.float64)
- p_pred = pred[marker_id]
- d = (p_pred - p_obs) * sqrt_w
- res.extend(d.tolist())
-
- # Root prior
- if cfg.include_root_prior:
- root_t_prior = np.asarray(template_state["root_t"], dtype=np.float64)
- try:
- from scipy.spatial.transform import Rotation
- root_rvec_prior = Rotation.from_matrix(template_state["root_R"]).as_rotvec()
- except Exception:
- root_rvec_prior = np.zeros(3, dtype=np.float64)
-
- root_t = x[0:3]
- root_rvec = x[3:6]
- w = math.sqrt(max(1e-12, cfg.root_pose_prior_weight))
- res.extend(((root_t - root_t_prior) * w).tolist())
- res.extend(((root_rvec - root_rvec_prior) * w).tolist())
-
- # Joint priors
- if cfg.include_joint_prior:
- idx = 6
- w = math.sqrt(max(1e-12, cfg.joint_prior_weight))
- for var in active_joint_vars:
- if idx >= len(x):
- break
- q = x[idx]
- q0 = template_state["joint_defaults"].get(var, 0.0)
- # Revolute angles should not wrap too aggressively during optimization,
- # but the prior itself should be short-arc.
- if abs(q0) <= math.pi * 2.5:
- dq = wrap_angle_rad(float(q - q0))
- else:
- dq = float(q - q0)
- res.append(dq * w)
- idx += 1
-
- return np.asarray(res, dtype=np.float64)
-
-
-def optimize_stage(
- link_infos: Dict[str, LinkInfo],
- observed_markers: Dict[int, Dict[str, Any]],
- template_state: Dict[str, Any],
- active_joint_vars: List[str],
- cfg: EstimationConfig,
-) -> Tuple[Dict[str, Any], Dict[str, Any]]:
- try:
- from scipy.optimize import least_squares
- except ImportError:
- print("[WARN] scipy not available; returning template state for this stage.")
- return template_state, {"success": False, "message": "scipy unavailable"}
-
- x0 = stage_variables_to_vector(template_state, active_joint_vars)
-
- print(f"[INFO] Stage variables: {len(x0)} (root pose + {len(active_joint_vars)} active joints)")
-
- result = least_squares(
- lambda x: residuals_stage(x, link_infos, observed_markers, template_state, active_joint_vars, cfg),
- x0,
- loss=cfg.robust_loss,
- f_scale=1.0,
- max_nfev=cfg.max_iterations,
- verbose=1,
- )
-
- final_state = vector_to_stage_state(result.x, template_state, active_joint_vars)
- opt_info = {
- "cost": float(np.sum(result.fun ** 2)),
- "success": bool(result.success),
- "status": int(result.status),
- "message": str(result.message),
- "nfev": int(result.nfev),
- "njev": int(getattr(result, "njev", -1)),
- }
- final_state["_optimizer"] = opt_info
- return final_state, opt_info
-
-
-def compute_error_stats(
- link_infos: Dict[str, LinkInfo],
- state: Dict[str, Any],
- observed_markers: Dict[int, Dict[str, Any]],
-) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
- pred = predict_marker_positions(link_infos, state)
-
- marker_reports: List[Dict[str, Any]] = []
- errors = []
-
- marker_to_link = {}
- marker_meta = {}
- for link in link_infos.values():
- for m in link.markers:
- marker_to_link[m.marker_id] = link.name
- marker_meta[m.marker_id] = m
-
- for marker_id, obs in observed_markers.items():
- if marker_id not in pred:
- continue
-
- p_obs = np.asarray(obs["position_m"], dtype=np.float64)
- p_pred = pred[marker_id]
- err = p_pred - p_obs
- err_norm = float(np.linalg.norm(err))
- errors.append(err_norm)
-
- m = marker_meta.get(marker_id)
- marker_reports.append(
- {
- "marker_id": int(marker_id),
- "link": marker_to_link.get(marker_id, "unknown"),
- "observed_position_m": [float(x) for x in p_obs],
- "predicted_position_m": [float(x) for x in p_pred],
- "error_m": [float(x) for x in err],
- "error_norm_m": err_norm,
- "error_norm_mm": err_norm * 1000.0,
- "marker_size": None if m is None else m.size,
- }
- )
-
- if errors:
- arr = np.asarray(errors, dtype=np.float64)
- stats = {
- "num_markers_used": int(len(errors)),
- "mean_error_m": float(np.mean(arr)),
- "median_error_m": float(np.median(arr)),
- "rms_error_m": float(np.sqrt(np.mean(arr ** 2))),
- "worst_error_m": float(np.max(arr)),
- "p80_error_m": float(np.quantile(arr, 0.80)),
- "p90_error_m": float(np.quantile(arr, 0.90)),
- }
- else:
- stats = {
- "num_markers_used": 0,
- "mean_error_m": None,
- "median_error_m": None,
- "rms_error_m": None,
- "worst_error_m": None,
- "p80_error_m": None,
- "p90_error_m": None,
- }
-
- return marker_reports, stats
-
-
-def print_stage_stats(stage_idx: int, depth: int, stage_result: StageResult) -> None:
- print(
- f"[INFO] Stage {stage_idx} (depth={depth}) | "
- f"active_links={len(stage_result.active_links)} | "
- f"active_joint_vars={len(stage_result.active_joint_vars)} | "
- f"markers={stage_result.num_markers_used}"
- )
- if stage_result.mean_error_m is not None:
- print(
- f"[INFO] Stage {stage_idx} error: "
- f"mean={stage_result.mean_error_m*1000.0:.2f} mm | "
- f"rms={stage_result.rms_error_m*1000.0:.2f} mm | "
- f"worst={stage_result.worst_error_m*1000.0:.2f} mm"
- )
- if stage_result.optimizer_info:
- print(
- f"[INFO] Stage {stage_idx} optimizer: "
- f"success={stage_result.optimizer_info.get('success', False)} | "
- f"nfev={stage_result.optimizer_info.get('nfev', -1)} | "
- f"cost={stage_result.optimizer_info.get('cost', 0.0):.6f}"
- )
-
-
-def sequential_optimize_state(
- robot_data: Dict[str, Any],
- link_infos: Dict[str, LinkInfo],
- observed_markers: Dict[int, Dict[str, Any]],
- initial_state: Dict[str, Any],
- cfg: EstimationConfig,
-) -> Tuple[Dict[str, Any], List[StageResult]]:
- """
- Sequential prefix optimization:
- - solve links up to depth 0,
- - then depth 1,
- - then depth 2, ...
- Each new stage starts from the previous state and only activates the
- joint variables that belong to the current prefix.
- """
- depths = compute_link_depths(link_infos)
- max_depth = max(depths.values()) if depths else 0
-
- current_state = {
- "root_R": initial_state["root_R"].copy(),
- "root_t": initial_state["root_t"].copy(),
- "joint_defaults": dict(initial_state["joint_defaults"]),
- "joint_values": dict(initial_state["joint_values"]),
- }
-
- stage_results: List[StageResult] = []
-
- for depth in range(0, max_depth + 1):
- active_links = active_links_for_depth(link_infos, depths, depth)
- active_joint_vars = active_joint_vars_for_links(link_infos, active_links)
- active_obs = active_observations_for_links(link_infos, observed_markers, active_links)
-
- # If a stage introduces no observations, we still may want the chain prefix,
- # but there is nothing to fit against. In that case just keep the current state.
- if len(active_obs) == 0:
- stage_state = current_state
- stage_res = StageResult(
- depth=depth,
- active_links=active_links,
- active_joint_vars=active_joint_vars,
- mean_error_m=None,
- rms_error_m=None,
- worst_error_m=None,
- num_markers_used=0,
- optimizer_info={"skipped": True, "reason": "no active observations"},
- )
- stage_results.append(stage_res)
- if cfg.show_stage_reports:
- print_stage_stats(len(stage_results) - 1, depth, stage_res)
- continue
-
- # Optimize only the active prefix variables.
- template_state = current_state
- optimized_state, opt_info = optimize_stage(
- link_infos=link_infos,
- observed_markers=active_obs,
- template_state=template_state,
- active_joint_vars=active_joint_vars,
- cfg=cfg,
- )
-
- # Merge optimized active values into the running state.
- current_state["root_R"] = optimized_state["root_R"].copy()
- current_state["root_t"] = optimized_state["root_t"].copy()
- current_state["joint_values"].update(optimized_state["joint_values"])
-
- # Evaluate the active prefix after the stage.
- marker_reports, stats = compute_error_stats(link_infos, current_state, active_obs)
-
- stage_res = StageResult(
- depth=depth,
- active_links=active_links,
- active_joint_vars=active_joint_vars,
- mean_error_m=stats["mean_error_m"],
- rms_error_m=stats["rms_error_m"],
- worst_error_m=stats["worst_error_m"],
- num_markers_used=stats["num_markers_used"],
- optimizer_info=opt_info,
- )
- stage_results.append(stage_res)
-
- if cfg.show_stage_reports:
- print_stage_stats(len(stage_results) - 1, depth, stage_res)
-
- if stage_results:
- current_state["_optimizer"] = stage_results[-1].optimizer_info
- return current_state, stage_results
-
-
-# =============================================================================
-# Output
-# =============================================================================
-
-def state_to_json(
- robot_data: Dict[str, Any],
- link_infos: Dict[str, LinkInfo],
- state: Dict[str, Any],
- observed_markers: Dict[int, Dict[str, Any]],
- marker_reports: List[Dict[str, Any]],
- stats: Dict[str, Any],
- input_file: str,
- robot_file: str,
- stage_results: List[StageResult],
-) -> Dict[str, Any]:
- movements = {}
- for link_name in topological_links(link_infos):
- link = link_infos[link_name]
- if not link.joint_variable:
- continue
-
- q = state["joint_values"].get(link.joint_variable, state["joint_defaults"].get(link.joint_variable, 0.0))
- joint_type = (link.joint_type or "").strip().lower()
-
- if joint_type == "revolute":
- movements[link.joint_variable] = {
- "value_rad": float(q),
- "value_deg": float(math.degrees(q)),
- "joint_type": joint_type,
- "link": link_name,
- }
- elif joint_type == "linear":
- movements[link.joint_variable] = {
- "value_m": float(q),
- "value_mm": float(q * 1000.0),
- "joint_type": joint_type,
- "link": link_name,
- }
- else:
- movements[link.joint_variable] = {
- "value": float(q),
- "joint_type": joint_type,
- "link": link_name,
- }
-
- link_pose_entries = []
- cache: Dict[str, np.ndarray] = {}
- for link_name in topological_links(link_infos):
- T = world_to_link_transform(link_name, link_infos, state, cache)
- R = T[:3, :3]
- t = T[:3, 3]
- link = link_infos[link_name]
- num_used = sum(1 for m in link.markers if m.marker_id in observed_markers)
- link_pose_entries.append(
- {
- "link": link_name,
- "parent": link.parent,
- "position_m": [float(x) for x in t],
- "rotation_matrix": [[float(v) for v in row] for row in R],
- "euler_xyz_deg": [float(x) for x in matrix_to_euler_xyz(R)],
- "num_observed_markers": int(num_used),
- "num_markers_total": int(len(link.markers)),
- }
- )
-
- root_link = next((name for name, info in link_infos.items() if info.parent is None), None)
- root_T = make_transform(state["root_R"], state["root_t"])
-
- out = {
- "schema_version": "2.0",
- "created_utc": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
- "source": {
- "marker_positions_file": input_file,
- "robot_file": robot_file,
- },
- "summary": {
- "num_links": int(len(link_infos)),
- "num_observed_markers": int(len(observed_markers)),
- "num_link_markers": int(sum(len(li.markers) for li in link_infos.values())),
- "root_link": root_link,
- "optimizer": state.get("_optimizer", {}),
- "fit_stats": stats,
- "stages": [
- {
- "depth": int(s.depth),
- "active_links": s.active_links,
- "active_joint_vars": s.active_joint_vars,
- "mean_error_m": s.mean_error_m,
- "rms_error_m": s.rms_error_m,
- "worst_error_m": s.worst_error_m,
- "num_markers_used": int(s.num_markers_used),
- "optimizer_info": s.optimizer_info,
- }
- for s in stage_results
- ],
- },
- "world_pose": {
- "root_translation_m": [float(x) for x in root_T[:3, 3]],
- "root_rotation_matrix": [[float(v) for v in row] for row in root_T[:3, :3]],
- "root_euler_xyz_deg": [float(x) for x in matrix_to_euler_xyz(root_T[:3, :3])],
- },
- "movements": movements,
- "links": link_pose_entries,
- "markers": marker_reports,
- }
-
- return out
-
-
-# =============================================================================
-# Main
-# =============================================================================
-
-def main() -> None:
- parser = argparse.ArgumentParser(
- description="Sequential robot state estimation from optimized ArUco marker positions"
- )
- parser.add_argument(
- "optimized_markers",
- help="aruco_positions_optimized*.json"
- )
- parser.add_argument(
- "-robot", "--robot",
- required=True,
- help="robot.json"
- )
- parser.add_argument(
- "-out", "--out",
- default=None,
- help="Output robot_state.json path"
- )
- parser.add_argument(
- "--maxIterations",
- type=int,
- default=120,
- help="Maximum least-squares iterations per stage"
- )
- parser.add_argument(
- "--jointPriorWeight",
- type=float,
- default=0.05,
- help="Prior weight for joint variables"
- )
- parser.add_argument(
- "--rootPosePriorWeight",
- type=float,
- default=0.1,
- help="Prior weight for root pose"
- )
- parser.add_argument(
- "--markerBaseWeight",
- type=float,
- default=1.0,
- help="Base weight for marker residuals"
- )
- parser.add_argument(
- "--noJointPrior",
- action="store_true",
- help="Disable joint priors"
- )
- parser.add_argument(
- "--noRootPrior",
- action="store_true",
- help="Disable root pose prior"
- )
- parser.add_argument(
- "--noSequential",
- action="store_true",
- help="Disable sequential prefix optimization and do one global pass"
- )
-
- args = parser.parse_args()
-
- print("[STEP 1] Load robot.json and optimized marker positions...")
- robot_data = load_json(args.robot)
- link_infos, marker_by_id, issues = parse_robot(robot_data)
- observed_markers = load_json(args.optimized_markers)
- markers = observed_markers.get("markers", []) if isinstance(observed_markers, dict) else []
-
- observed_map: Dict[int, Dict[str, Any]] = {}
- for m in markers:
- marker_id = int(m["marker_id"])
- observed_map[marker_id] = m
-
- for issue in issues:
- print(issue)
-
- print(f"[INFO] Links: {len(link_infos)}")
- print(f"[INFO] Known robot markers: {len(marker_by_id)}")
- print(f"[INFO] Observed optimized markers: {len(observed_map)}")
-
- print("\n[STEP 2] Build initial robot state...")
- initial_state = build_initial_state(robot_data, link_infos, observed_map)
- print(f"[INFO] Initial root translation (m): {initial_state['root_t']}")
- print(f"[INFO] Initial joint defaults: {initial_state['joint_defaults']}")
-
- cfg = EstimationConfig(
- root_pose_prior_weight=float(args.rootPosePriorWeight),
- joint_prior_weight=float(args.jointPriorWeight),
- marker_base_weight=float(args.markerBaseWeight),
- robust_loss="soft_l1",
- max_iterations=int(args.maxIterations),
- include_joint_prior=not args.noJointPrior,
- include_root_prior=not args.noRootPrior,
- sequential=not args.noSequential,
- show_stage_reports=True,
- )
-
- print("\n[STEP 3] Estimate robot state...")
- if cfg.sequential:
- final_state, stage_results = sequential_optimize_state(
- robot_data=robot_data,
- link_infos=link_infos,
- observed_markers=observed_map,
- initial_state=initial_state,
- cfg=cfg,
- )
- else:
- # Fallback: single global stage over the entire kinematic tree.
- active_links = topological_links(link_infos)
- active_joint_vars = active_joint_vars_for_links(link_infos, active_links)
- final_state, opt_info = optimize_stage(
- link_infos=link_infos,
- observed_markers=observed_map,
- template_state=initial_state,
- active_joint_vars=active_joint_vars,
- cfg=cfg,
- )
- final_state["_optimizer"] = opt_info
- stage_results = []
-
- print("\n[STEP 4] Build report and save robot_state.json...")
- marker_reports, stats = compute_error_stats(link_infos, final_state, observed_map)
-
- out_data = state_to_json(
- robot_data=robot_data,
- link_infos=link_infos,
- state=final_state,
- observed_markers=observed_map,
- marker_reports=marker_reports,
- stats=stats,
- input_file=args.optimized_markers,
- robot_file=args.robot,
- stage_results=stage_results,
- )
-
- out_path = args.out
- if out_path is None:
- out_dir = os.path.dirname(args.optimized_markers) or "."
- out_path = os.path.join(out_dir, "robot_state.json")
-
- save_json(out_path, out_data)
-
- print(f"[INFO] Saved to {out_path}")
- if stats["mean_error_m"] is not None:
- print(f"[INFO] Mean marker error: {stats['mean_error_m']*1000.0:.2f} mm")
- print(f"[INFO] RMS marker error : {stats['rms_error_m']*1000.0:.2f} mm")
- print(f"[INFO] Worst marker error: {stats['worst_error_m']*1000.0:.2f} mm")
- else:
- print("[INFO] No marker error statistics available.")
-
-
-if __name__ == "__main__":
- main()
diff --git a/pipeline/4_robotState_estimation_v5.py b/pipeline/4_robotState_estimation_v5.py
deleted file mode 100644
index a5de588..0000000
--- a/pipeline/4_robotState_estimation_v5.py
+++ /dev/null
@@ -1,1330 +0,0 @@
-#!/usr/bin/env python3
-"""
-4_robotState_estimation_v5.py
-
-Sequential robot-state estimation from optimized 3D ArUco marker positions.
-
-Mathematical idea
------------------
-This script estimates a robot state q directly from already optimized marker
-positions p_i^obs (from aruco_positions_optimized*.json).
-
-The state is built incrementally along the kinematic chain:
- 1) estimate the root link pose from observed root-link markers
- 2) extend the active prefix link-by-link
- 3) for each newly activated joint, estimate its scalar variable by a geometric
- rule that matches the robot's degrees of freedom
-
-For each stage we keep the already estimated prefix fixed and only add the next
-link(s) from the chain. This is intentionally *not* a generic global optimizer
-by default. It is a deterministic geometric initializer that uses the robot
-structure and marker geometry directly.
-
-The stage logic is controlled by robot.json::state_pose_params:
- - numbers_of_Elements_to_consider_start
- - numbers_of_Elements_to_consider_final
- - solver_in_between_geometrical
- - solver_after_geometrical
-
-Geometric rules used here
--------------------------
-Rigid root / early prefix:
- - root link pose is estimated from its observed markers with weighted Kabsch
-
-Linear joint (slider):
- - the joint translates its whole descendant subtree along the joint axis
- - q is estimated as the weighted mean projection of observed minus predicted
- marker positions onto the world-space joint axis
-
-Revolute joint:
- - the joint rotates its descendant subtree around the joint axis
- - q is estimated by a coarse-to-fine 1D angular search that minimizes the
- weighted marker residual over the active prefix
-
-Optional solver
----------------
-If enabled in state_pose_params, a least-squares refinement can be run after a
-geometric stage or at the end. The geometric estimator remains the default and
-is the first-class method in this script.
-
-Important limitation
---------------------
-This script works from marker positions only. It does not yet use image-space
-marker orientation / normal / visibility cues. Those can be added later by
-feeding in raw detections and camera poses.
-
-Dependencies
-------------
-numpy, scipy (optional for refinement)
-"""
-
-from __future__ import annotations
-
-import argparse
-import copy
-import json
-import math
-import os
-import sys
-import time
-from dataclasses import dataclass
-from typing import Any, Dict, List, Optional, Tuple
-
-import numpy as np
-
-
-# =============================================================================
-# Path / JSON helpers
-# =============================================================================
-
-def resolve_path(path: str) -> str:
- path = os.path.expanduser(path)
- if os.path.isabs(path):
- return path
- return os.path.abspath(path)
-
-
-def load_json(path: str) -> Any:
- with open(resolve_path(path), 'r', encoding='utf-8') as f:
- return json.load(f)
-
-
-def save_json(path: str, data: Any) -> None:
- with open(resolve_path(path), 'w', encoding='utf-8') as f:
- json.dump(data, f, indent=2)
-
-
-# =============================================================================
-# Small math helpers
-# =============================================================================
-
-def safe_norm(v: np.ndarray, eps: float = 1e-12) -> float:
- return float(np.linalg.norm(v) + eps)
-
-
-def normalize(v: np.ndarray, eps: float = 1e-12) -> np.ndarray:
- v = np.asarray(v, dtype=np.float64)
- return v / safe_norm(v, eps)
-
-
-def wrap_angle_rad(a: float) -> float:
- return (a + math.pi) % (2.0 * math.pi) - math.pi
-
-
-def get_length_scale(robot_data: Dict[str, Any]) -> float:
- units = robot_data.get('units', {}) or {}
- length_unit = str(units.get('length', '')).strip().lower()
- if length_unit in ('mm', 'millimeter', 'millimeters'):
- return 1.0 / 1000.0
- if length_unit in ('cm', 'centimeter', 'centimeters'):
- return 1.0 / 100.0
- return 1.0
-
-
-def rotation_matrix_xyz(rx: float, ry: float, rz: float, degrees: bool = False) -> np.ndarray:
- """Rotation order: X then Y then Z."""
- if degrees:
- rx, ry, rz = math.radians(rx), math.radians(ry), math.radians(rz)
-
- cx, sx = math.cos(rx), math.sin(rx)
- cy, sy = math.cos(ry), math.sin(ry)
- cz, sz = math.cos(rz), math.sin(rz)
-
- rx_m = np.array([[1.0, 0.0, 0.0],
- [0.0, cx, -sx],
- [0.0, sx, cx]], dtype=np.float64)
- ry_m = np.array([[cy, 0.0, sy],
- [0.0, 1.0, 0.0],
- [-sy, 0.0, cy]], dtype=np.float64)
- rz_m = np.array([[cz, -sz, 0.0],
- [sz, cz, 0.0],
- [0.0, 0.0, 1.0]], dtype=np.float64)
- return rz_m @ ry_m @ rx_m
-
-
-def axis_angle_rotation(axis: np.ndarray, angle_rad: float) -> np.ndarray:
- axis = normalize(axis)
- x, y, z = axis
- c = math.cos(angle_rad)
- s = math.sin(angle_rad)
- C = 1.0 - c
- return np.array([
- [c + x * x * C, x * y * C - z * s, x * z * C + y * s],
- [y * x * C + z * s, c + y * y * C, y * z * C - x * s],
- [z * x * C - y * s, z * y * C + x * s, c + z * z * C]
- ], dtype=np.float64)
-
-
-def make_transform(R: Optional[np.ndarray] = None, t: Optional[np.ndarray] = None) -> np.ndarray:
- T = np.eye(4, dtype=np.float64)
- if R is not None:
- T[:3, :3] = np.asarray(R, dtype=np.float64)
- if t is not None:
- T[:3, 3] = np.asarray(t, dtype=np.float64).reshape(3)
- return T
-
-
-def transform_point(T: np.ndarray, p: np.ndarray) -> np.ndarray:
- p4 = np.ones(4, dtype=np.float64)
- p4[:3] = np.asarray(p, dtype=np.float64).reshape(3)
- return (T @ p4)[:3]
-
-
-def matrix_to_euler_xyz(R: np.ndarray) -> np.ndarray:
- R = np.asarray(R, dtype=np.float64)
- sy = math.sqrt(R[0, 0] * R[0, 0] + R[1, 0] * R[1, 0])
- singular = sy < 1e-9
-
- if not singular:
- x = math.atan2(R[2, 1], R[2, 2])
- y = math.atan2(-R[2, 0], sy)
- z = math.atan2(R[1, 0], R[0, 0])
- else:
- x = math.atan2(-R[1, 2], R[1, 1])
- y = math.atan2(-R[2, 0], sy)
- z = 0.0
- return np.degrees([x, y, z])
-
-
-def kabsch(P: np.ndarray, Q: np.ndarray, W: Optional[np.ndarray] = None) -> Tuple[np.ndarray, np.ndarray]:
- """Find R, t such that R @ P + t ≈ Q."""
- P = np.asarray(P, dtype=np.float64)
- Q = np.asarray(Q, dtype=np.float64)
- assert P.shape == Q.shape and P.shape[1] == 3
-
- if W is None:
- W = np.ones(len(P), dtype=np.float64)
- W = np.asarray(W, dtype=np.float64).reshape(-1)
- W = np.maximum(W, 1e-12)
- W = W / np.sum(W)
-
- p_cent = np.sum(P * W[:, None], axis=0)
- q_cent = np.sum(Q * W[:, None], axis=0)
-
- P0 = P - p_cent
- Q0 = Q - q_cent
- H = (P0 * W[:, None]).T @ Q0
-
- U, S, Vt = np.linalg.svd(H)
- R = Vt.T @ U.T
- if np.linalg.det(R) < 0:
- Vt[-1, :] *= -1.0
- R = Vt.T @ U.T
- t = q_cent - R @ p_cent
- return R, t
-
-
-# =============================================================================
-# Data structures
-# =============================================================================
-
-@dataclass
-class MarkerInfo:
- marker_id: int
- link_name: str
- local_pos_m: np.ndarray
- size: Optional[float] = None
- normal: Optional[np.ndarray] = None
- name: str = ''
-
-
-@dataclass
-class LinkInfo:
- name: str
- parent: Optional[str]
- joint_type: Optional[str]
- joint_axis: Optional[np.ndarray]
- joint_origin_m: np.ndarray
- joint_rotation_deg: np.ndarray
- joint_variable: Optional[str]
- mount_position_m: np.ndarray
- mount_rotation_deg: np.ndarray
- markers: List[MarkerInfo]
-
-
-@dataclass
-class StageResult:
- stage_idx: int
- active_links: List[str]
- active_joint_vars: List[str]
- num_markers_used: int
- mean_error_m: Optional[float]
- rms_error_m: Optional[float]
- worst_error_m: Optional[float]
- method: str
- optimizer_info: Dict[str, Any]
-
-
-@dataclass
-class StatePoseParams:
- numbers_of_elements_to_consider_start: int = 4
- numbers_of_elements_to_consider_final: int = 5
- solver_in_between_geometrical: bool = False
- solver_after_geometrical: bool = False
- geometric_passes_per_stage: int = 2
- revolute_search_coarse_deg: float = 5.0
- revolute_search_fine_deg: float = 1.0
- root_pose_min_markers: int = 3
-
-
-@dataclass
-class EstimationConfig:
- marker_base_weight: float = 1.0
- root_pose_prior_weight: float = 0.0
- joint_prior_weight: float = 0.0
- robust_loss: str = 'soft_l1'
- max_iterations: int = 120
- show_stage_reports: bool = True
- use_geometric_prefix: bool = True
-
-
-# =============================================================================
-# Robot parsing
-# =============================================================================
-
-def parse_robot(robot_data: Dict[str, Any]) -> Tuple[Dict[str, LinkInfo], Dict[int, MarkerInfo], List[str]]:
- length_scale = get_length_scale(robot_data)
- links = robot_data.get('links', {}) or {}
-
- link_infos: Dict[str, LinkInfo] = {}
- marker_by_id: Dict[int, MarkerInfo] = {}
- issues: List[str] = []
-
- for link_name, link_data in links.items():
- parent = link_data.get('parent', None)
-
- joint = link_data.get('jointToParent', {}) or {}
- joint_type = joint.get('type', None)
-
- axis = joint.get('axis', None)
- if axis is not None:
- axis = np.asarray(axis, dtype=np.float64)
- if safe_norm(axis) > 1e-12:
- axis = normalize(axis)
- else:
- axis = None
-
- joint_origin = np.asarray(joint.get('origin', [0, 0, 0]), dtype=np.float64) * float(length_scale)
- joint_rotation_deg = np.asarray(joint.get('rotation', [0, 0, 0]), dtype=np.float64)
- joint_variable = joint.get('variable', None)
-
- mount_position = np.asarray(link_data.get('mountPosition', [0, 0, 0]), dtype=np.float64) * float(length_scale)
- mount_rotation_deg = np.asarray(link_data.get('mountRotation', [0, 0, 0]), dtype=np.float64)
-
- markers_data = link_data.get('markers', []) or []
- markers: List[MarkerInfo] = []
- for idx, m in enumerate(markers_data):
- marker_id = int(m.get('id', -1))
- pos = m.get('position', None)
-
- if marker_id < 0 or pos is None or len(pos) != 3:
- issues.append(f"[WARN] link='{link_name}': skipped invalid marker entry at index {idx}")
- continue
-
- if marker_id in marker_by_id:
- issues.append(
- f"[WARN] duplicate marker ID {marker_id} in link '{link_name}' "
- f"(already in '{marker_by_id[marker_id].link_name}'); using first occurrence"
- )
- continue
-
- normal = None
- if isinstance(m.get('normal', None), list) and len(m['normal']) == 3:
- normal = np.asarray(m['normal'], dtype=np.float64)
-
- marker = MarkerInfo(
- marker_id=marker_id,
- link_name=link_name,
- local_pos_m=np.asarray(pos, dtype=np.float64) * float(length_scale),
- size=m.get('size', None),
- normal=normal,
- name=str(m.get('name', f'marker_{marker_id}')),
- )
- markers.append(marker)
- marker_by_id[marker_id] = marker
-
- link_infos[link_name] = LinkInfo(
- name=link_name,
- parent=parent,
- joint_type=joint_type,
- joint_axis=axis,
- joint_origin_m=joint_origin,
- joint_rotation_deg=joint_rotation_deg,
- joint_variable=joint_variable,
- mount_position_m=mount_position,
- mount_rotation_deg=mount_rotation_deg,
- markers=markers,
- )
-
- return link_infos, marker_by_id, issues
-
-
-def topological_links(link_infos: Dict[str, LinkInfo]) -> List[str]:
- children = {name: [] for name in link_infos}
- roots = []
- for name, info in link_infos.items():
- if info.parent is None:
- roots.append(name)
- else:
- children.setdefault(info.parent, []).append(name)
-
- order: List[str] = []
- queue = list(roots)
- seen = set()
-
- while queue:
- cur = queue.pop(0)
- if cur in seen:
- continue
- seen.add(cur)
- order.append(cur)
- for ch in children.get(cur, []):
- queue.append(ch)
-
- for name in link_infos:
- if name not in seen:
- order.append(name)
- return order
-
-
-def compute_children_map(link_infos: Dict[str, LinkInfo]) -> Dict[str, List[str]]:
- children: Dict[str, List[str]] = {name: [] for name in link_infos}
- for name, info in link_infos.items():
- if info.parent is not None:
- children.setdefault(info.parent, []).append(name)
- return children
-
-
-def compute_link_depths(link_infos: Dict[str, LinkInfo]) -> Dict[str, int]:
- depths: Dict[str, int] = {}
-
- def depth_of(name: str) -> int:
- if name in depths:
- return depths[name]
- info = link_infos[name]
- if info.parent is None:
- depths[name] = 0
- else:
- depths[name] = depth_of(info.parent) + 1
- return depths[name]
-
- for name in link_infos:
- depth_of(name)
- return depths
-
-
-# =============================================================================
-# State pose params
-# =============================================================================
-
-def load_state_pose_params(robot_data: Dict[str, Any]) -> StatePoseParams:
- raw = robot_data.get('state_pose_params', {}) or {}
- return StatePoseParams(
- numbers_of_elements_to_consider_start=int(raw.get('numbers_of_Elements_to_consider_start', 4)),
- numbers_of_elements_to_consider_final=int(raw.get('numbers_of_Elements_to_consider_final', 5)),
- solver_in_between_geometrical=bool(raw.get('solver_in_between_geometrical', False)),
- solver_after_geometrical=bool(raw.get('solver_after_geometrical', False)),
- geometric_passes_per_stage=max(1, int(raw.get('geometric_passes_per_stage', 2))),
- revolute_search_coarse_deg=float(raw.get('revolute_search_coarse_deg', 5.0)),
- revolute_search_fine_deg=float(raw.get('revolute_search_fine_deg', 1.0)),
- root_pose_min_markers=max(1, int(raw.get('root_pose_min_markers', 3))),
- )
-
-
-# =============================================================================
-# Observations
-# =============================================================================
-
-def load_observed_markers(optimized_markers_file: str) -> Dict[int, Dict[str, Any]]:
- data = load_json(optimized_markers_file)
- if isinstance(data, dict):
- markers = data.get('markers', []) or []
- elif isinstance(data, list):
- markers = data
- else:
- markers = []
-
- observed: Dict[int, Dict[str, Any]] = {}
- for m in markers:
- if not isinstance(m, dict):
- continue
- if 'marker_id' not in m:
- continue
- marker_id = int(m['marker_id'])
- pos = m.get('position_m', None)
- if pos is None or len(pos) != 3:
- continue
- obs = dict(m)
- obs['marker_id'] = marker_id
- obs['position_m'] = np.asarray(pos, dtype=np.float64)
- observed[marker_id] = obs
- return observed
-
-
-def marker_weight(marker_info: MarkerInfo, base_weight: float = 1.0, ref_size: float = 25.0) -> float:
- w = base_weight
- if marker_info.size is not None:
- try:
- size = float(marker_info.size)
- if size > 0:
- w *= max(0.35, math.sqrt(size / ref_size))
- except Exception:
- pass
- return w
-
-
-# =============================================================================
-# Forward kinematics
-# =============================================================================
-
-def link_static_transform(link: LinkInfo) -> np.ndarray:
- Rm = rotation_matrix_xyz(*link.mount_rotation_deg, degrees=True)
- return make_transform(Rm, link.mount_position_m)
-
-
-def joint_motion_transform(link: LinkInfo, q_value: float) -> np.ndarray:
- joint_type = (link.joint_type or '').strip().lower()
- if link.joint_axis is None:
- return np.eye(4, dtype=np.float64)
-
- axis = normalize(link.joint_axis)
- if joint_type == 'linear':
- return make_transform(np.eye(3), axis * float(q_value))
- if joint_type == 'revolute':
- return make_transform(axis_angle_rotation(axis, float(q_value)), np.zeros(3))
- return np.eye(4, dtype=np.float64)
-
-
-def world_to_link_transform(
- link_name: str,
- link_infos: Dict[str, LinkInfo],
- state: Dict[str, Any],
- cache: Dict[Tuple[str, Tuple[Tuple[str, float], ...]], np.ndarray],
- joint_override: Optional[Dict[str, float]] = None,
-) -> np.ndarray:
- override_items = tuple(sorted((joint_override or {}).items()))
- cache_key = (link_name, override_items)
- if cache_key in cache:
- return cache[cache_key]
-
- link = link_infos[link_name]
- if link.parent is None:
- T = make_transform(state['root_R'], state['root_t'])
- cache[cache_key] = T
- return T
-
- parent_T = world_to_link_transform(link.parent, link_infos, state, cache, joint_override=joint_override)
-
- T = parent_T @ make_transform(np.eye(3), link.joint_origin_m)
-
- joint_values = state['joint_values']
- q = joint_override.get(link.name, joint_values.get(link.joint_variable, 0.0)) if joint_override else joint_values.get(link.joint_variable, 0.0)
- T = T @ joint_motion_transform(link, q)
-
- R_static = rotation_matrix_xyz(*link.joint_rotation_deg, degrees=True)
- T = T @ make_transform(R_static, np.zeros(3))
-
- T = T @ link_static_transform(link)
-
- cache[cache_key] = T
- return T
-
-
-def predict_marker_positions(
- link_infos: Dict[str, LinkInfo],
- state: Dict[str, Any],
- joint_override: Optional[Dict[str, float]] = None,
- active_links: Optional[set[str]] = None,
-) -> Dict[int, np.ndarray]:
- pred: Dict[int, np.ndarray] = {}
- cache: Dict[Tuple[str, Tuple[Tuple[str, float], ...]], np.ndarray] = {}
- for link_name, link in link_infos.items():
- if active_links is not None and link_name not in active_links:
- continue
- T = world_to_link_transform(link_name, link_infos, state, cache, joint_override=joint_override)
- for marker in link.markers:
- pred[marker.marker_id] = transform_point(T, marker.local_pos_m)
- return pred
-
-
-def collect_observations_by_link(
- observed_markers: Dict[int, Dict[str, Any]],
- active_links: Optional[set[str]] = None,
-) -> Dict[str, List[int]]:
- by_link: Dict[str, List[int]] = {}
- for marker_id, obs in observed_markers.items():
- link = obs.get('link', None)
- if link is None or link == 'unknown':
- continue
- if active_links is not None and link not in active_links:
- continue
- by_link.setdefault(link, []).append(marker_id)
- return by_link
-
-
-# =============================================================================
-# Initial geometric root pose
-# =============================================================================
-
-def initial_root_pose(
- root_link: LinkInfo,
- observed_markers: Dict[int, Dict[str, Any]],
- min_markers: int = 3,
-) -> Tuple[np.ndarray, np.ndarray]:
- P = []
- Q = []
- W = []
-
- for marker in root_link.markers:
- if marker.marker_id in observed_markers:
- obs = np.asarray(observed_markers[marker.marker_id]['position_m'], dtype=np.float64)
- P.append(marker.local_pos_m)
- Q.append(obs)
- W.append(marker_weight(marker))
-
- if len(P) >= min_markers:
- return kabsch(np.asarray(P), np.asarray(Q), np.asarray(W))
-
- if len(P) >= 1:
- # Weak fallback: keep world axes and place root so the first marker matches.
- marker = root_link.markers[0]
- obs = np.asarray(observed_markers[marker.marker_id]['position_m'], dtype=np.float64)
- return np.eye(3, dtype=np.float64), obs - marker.local_pos_m
-
- return np.eye(3, dtype=np.float64), np.zeros(3, dtype=np.float64)
-
-
-# =============================================================================
-# Subtree helpers
-# =============================================================================
-
-def subtree_links(link_name: str, children_map: Dict[str, List[str]], active_links: set[str]) -> List[str]:
- out: List[str] = []
- queue = [link_name]
- while queue:
- cur = queue.pop(0)
- if cur not in active_links:
- continue
- out.append(cur)
- for ch in children_map.get(cur, []):
- if ch in active_links:
- queue.append(ch)
- return out
-
-
-def marker_ids_in_links(link_infos: Dict[str, LinkInfo], links: List[str]) -> List[int]:
- ids: List[int] = []
- for link_name in links:
- for m in link_infos[link_name].markers:
- ids.append(m.marker_id)
- return ids
-
-
-# =============================================================================
-# Geometric joint estimation
-# =============================================================================
-
-def current_joint_value(state: Dict[str, Any], link: LinkInfo) -> float:
- return float(state['joint_values'].get(link.joint_variable, 0.0)) if link.joint_variable else 0.0
-
-
-def world_joint_axis_for_link(
- link_name: str,
- link_infos: Dict[str, LinkInfo],
- state: Dict[str, Any],
-) -> np.ndarray:
- link = link_infos[link_name]
- if link.parent is None or link.joint_axis is None:
- return np.array([1.0, 0.0, 0.0], dtype=np.float64)
-
- cache: Dict[Tuple[str, Tuple[Tuple[str, float], ...]], np.ndarray] = {}
- parent_T = world_to_link_transform(link.parent, link_infos, state, cache)
- axis_world = parent_T[:3, :3] @ normalize(link.joint_axis)
- return normalize(axis_world)
-
-
-def estimate_linear_joint_value(
- link_name: str,
- link_infos: Dict[str, LinkInfo],
- state: Dict[str, Any],
- observed_markers: Dict[int, Dict[str, Any]],
- active_links: set[str],
- children_map: Dict[str, List[str]],
-) -> Tuple[float, Dict[str, Any]]:
- link = link_infos[link_name]
- if link.joint_variable is None:
- return 0.0, {'reason': 'no_joint_variable'}
-
- subtree = subtree_links(link_name, children_map, active_links)
- obs_ids = [mid for mid in marker_ids_in_links(link_infos, subtree) if mid in observed_markers]
- if not obs_ids:
- return current_joint_value(state, link), {'reason': 'no_observations'}
-
- axis_world = world_joint_axis_for_link(link_name, link_infos, state)
-
- # Evaluate current prediction with this joint forced to zero.
- override = {link_name: 0.0}
- pred0 = predict_marker_positions(link_infos, state, joint_override=override, active_links=active_links)
-
- num = 0.0
- den = 0.0
- used = 0
- per_marker = []
- for mid in obs_ids:
- if mid not in pred0:
- continue
- marker = next((m for m in link_infos[observed_markers[mid]['link']].markers if m.marker_id == mid), None)
- w = marker_weight(marker) if marker is not None else 1.0
- p_obs = np.asarray(observed_markers[mid]['position_m'], dtype=np.float64)
- p0 = pred0[mid]
- q_i = float(np.dot(axis_world, p_obs - p0))
- num += w * q_i
- den += w
- used += 1
- per_marker.append({'marker_id': mid, 'q_i': q_i, 'weight': w})
-
- if den <= 1e-12:
- return current_joint_value(state, link), {'reason': 'zero_weight'}
-
- q = num / den
- return float(q), {
- 'reason': 'weighted_projection',
- 'used_markers': used,
- 'axis_world': [float(x) for x in axis_world],
- 'per_marker': per_marker,
- }
-
-
-def marker_residual_error_for_state(
- link_infos: Dict[str, LinkInfo],
- state: Dict[str, Any],
- observed_markers: Dict[int, Dict[str, Any]],
- active_links: Optional[set[str]] = None,
-) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
- pred = predict_marker_positions(link_infos, state, active_links=active_links)
-
- marker_reports: List[Dict[str, Any]] = []
- errors = []
- for marker_id, obs in observed_markers.items():
- if marker_id not in pred:
- continue
- p_obs = np.asarray(obs['position_m'], dtype=np.float64)
- p_pred = pred[marker_id]
- err = p_pred - p_obs
- err_norm = float(np.linalg.norm(err))
- errors.append(err_norm)
- marker_reports.append({
- 'marker_id': int(marker_id),
- 'link': obs.get('link', 'unknown'),
- 'observed_position_m': [float(x) for x in p_obs],
- 'predicted_position_m': [float(x) for x in p_pred],
- 'error_m': [float(x) for x in err],
- 'error_norm_m': err_norm,
- 'error_norm_mm': err_norm * 1000.0,
- 'marker_size': obs.get('position_mm', None),
- })
-
- if errors:
- arr = np.asarray(errors, dtype=np.float64)
- stats = {
- 'num_markers_used': int(len(errors)),
- 'mean_error_m': float(np.mean(arr)),
- 'median_error_m': float(np.median(arr)),
- 'rms_error_m': float(np.sqrt(np.mean(arr ** 2))),
- 'worst_error_m': float(np.max(arr)),
- 'p80_error_m': float(np.quantile(arr, 0.80)),
- 'p90_error_m': float(np.quantile(arr, 0.90)),
- }
- else:
- stats = {
- 'num_markers_used': 0,
- 'mean_error_m': None,
- 'median_error_m': None,
- 'rms_error_m': None,
- 'worst_error_m': None,
- 'p80_error_m': None,
- 'p90_error_m': None,
- }
-
- return marker_reports, stats
-
-
-def revolute_weighted_error(
- link_name: str,
- q: float,
- link_infos: Dict[str, LinkInfo],
- state: Dict[str, Any],
- observed_markers: Dict[int, Dict[str, Any]],
- active_links: set[str],
- children_map: Dict[str, List[str]],
-) -> float:
- link = link_infos[link_name]
- if link.joint_variable is None:
- return float('inf')
-
- subtree = subtree_links(link_name, children_map, active_links)
- obs_ids = [mid for mid in marker_ids_in_links(link_infos, subtree) if mid in observed_markers]
- if not obs_ids:
- return float('inf')
-
- override = {link_name: q}
- pred = predict_marker_positions(link_infos, state, joint_override=override, active_links=active_links)
-
- err = 0.0
- wsum = 0.0
- for mid in obs_ids:
- if mid not in pred:
- continue
- marker = next((m for m in link_infos[observed_markers[mid]['link']].markers if m.marker_id == mid), None)
- w = marker_weight(marker) if marker is not None else 1.0
- d = pred[mid] - np.asarray(observed_markers[mid]['position_m'], dtype=np.float64)
- err += w * float(np.dot(d, d))
- wsum += w
-
- if wsum <= 1e-12:
- return float('inf')
- return err / wsum
-
-
-def estimate_revolute_joint_value(
- link_name: str,
- link_infos: Dict[str, LinkInfo],
- state: Dict[str, Any],
- observed_markers: Dict[int, Dict[str, Any]],
- active_links: set[str],
- children_map: Dict[str, List[str]],
- coarse_step_deg: float = 5.0,
- fine_step_deg: float = 1.0,
-) -> Tuple[float, Dict[str, Any]]:
- link = link_infos[link_name]
- if link.joint_variable is None:
- return 0.0, {'reason': 'no_joint_variable'}
-
- subtree = subtree_links(link_name, children_map, active_links)
- obs_ids = [mid for mid in marker_ids_in_links(link_infos, subtree) if mid in observed_markers]
- if not obs_ids:
- return current_joint_value(state, link), {'reason': 'no_observations'}
-
- q0 = current_joint_value(state, link)
- best_q = q0
- best_e = float('inf')
- used = len(obs_ids)
-
- # Coarse-to-fine search around the current value, but still wide enough to catch flips.
- centers = [q0, 0.0]
- span_list = [math.pi, math.pi / 4.0, math.pi / 16.0]
- step_list = [math.radians(coarse_step_deg), math.radians(fine_step_deg), math.radians(max(0.25, fine_step_deg / 2.0))]
-
- for center in centers:
- for span, step in zip(span_list, step_list):
- if step <= 0:
- continue
- qs = np.arange(center - span, center + span + 0.5 * step, step)
- for q in qs:
- e = revolute_weighted_error(link_name, float(q), link_infos, state, observed_markers, active_links, children_map)
- if e < best_e:
- best_e = e
- best_q = float(q)
- center = best_q
-
- return wrap_angle_rad(best_q), {
- 'reason': 'coarse_to_fine_scan',
- 'used_markers': used,
- 'best_error': best_e,
- 'q0': q0,
- 'search_span_rad': math.pi,
- }
-
-
-# =============================================================================
-# Geometric prefix estimation
-# =============================================================================
-
-def build_state_template(link_infos: Dict[str, LinkInfo]) -> Dict[str, Any]:
- return {
- 'root_R': np.eye(3, dtype=np.float64),
- 'root_t': np.zeros(3, dtype=np.float64),
- 'joint_values': {li.joint_variable: 0.0 for li in link_infos.values() if li.joint_variable},
- }
-
-
-def copy_state(state: Dict[str, Any]) -> Dict[str, Any]:
- return {
- 'root_R': np.asarray(state['root_R'], dtype=np.float64).copy(),
- 'root_t': np.asarray(state['root_t'], dtype=np.float64).copy(),
- 'joint_values': dict(state.get('joint_values', {})),
- }
-
-
-def active_observations_for_links(
- observed_markers: Dict[int, Dict[str, Any]],
- active_links: set[str],
-) -> Dict[int, Dict[str, Any]]:
- out: Dict[int, Dict[str, Any]] = {}
- for marker_id, obs in observed_markers.items():
- link_name = obs.get('link', None)
- if link_name in active_links:
- out[marker_id] = obs
- return out
-
-
-def optimize_geometric_prefix(
- link_infos: Dict[str, LinkInfo],
- observed_markers: Dict[int, Dict[str, Any]],
- initial_state: Dict[str, Any],
- active_links: List[str],
- cfg: StatePoseParams,
-) -> Tuple[Dict[str, Any], Dict[str, Any]]:
- active_set = set(active_links)
- children_map = compute_children_map(link_infos)
- stage_obs = active_observations_for_links(observed_markers, active_set)
-
- state = copy_state(initial_state)
- stage_info: Dict[str, Any] = {
- 'method': 'geometric_prefix',
- 'active_links': active_links,
- 'active_observations': len(stage_obs),
- 'joint_updates': [],
- }
-
- # Root pose is refreshed from the root markers if possible.
- root_link_name = next((ln for ln in active_links if link_infos[ln].parent is None), None)
- if root_link_name is not None:
- root_link = link_infos[root_link_name]
- root_R, root_t = initial_root_pose(root_link, stage_obs, min_markers=cfg.root_pose_min_markers)
- state['root_R'] = root_R
- state['root_t'] = root_t
- stage_info['root_link'] = root_link_name
- stage_info['root_pose_source'] = 'kabsch_root_markers' if len([m for m in root_link.markers if m.marker_id in stage_obs]) >= cfg.root_pose_min_markers else 'weak_single_marker_fallback'
-
- # Forward geometric passes.
- active_joint_links = [ln for ln in active_links if link_infos[ln].parent is not None]
- for pass_idx in range(cfg.geometric_passes_per_stage):
- pass_updates = []
- for link_name in active_joint_links:
- link = link_infos[link_name]
- jt = (link.joint_type or '').strip().lower()
- if jt == 'linear':
- q_old = current_joint_value(state, link)
- q_new, info = estimate_linear_joint_value(link_name, link_infos, state, stage_obs, active_set, children_map)
- state['joint_values'][link.joint_variable] = q_new
- pass_updates.append({'link': link_name, 'joint_variable': link.joint_variable, 'joint_type': jt, 'old': q_old, 'new': q_new, 'info': info})
- elif jt == 'revolute':
- q_old = current_joint_value(state, link)
- q_new, info = estimate_revolute_joint_value(
- link_name, link_infos, state, stage_obs, active_set, children_map,
- coarse_step_deg=cfg.revolute_search_coarse_deg,
- fine_step_deg=cfg.revolute_search_fine_deg,
- )
- state['joint_values'][link.joint_variable] = q_new
- pass_updates.append({'link': link_name, 'joint_variable': link.joint_variable, 'joint_type': jt, 'old': q_old, 'new': q_new, 'info': info})
- stage_info[f'pass_{pass_idx+1}_updates'] = pass_updates
-
- marker_reports, stats = marker_residual_error_for_state(link_infos, state, stage_obs, active_links=active_set)
- stage_info['fit_stats'] = stats
- stage_info['num_markers_used'] = stats['num_markers_used']
- return state, stage_info
-
-
-# =============================================================================
-# Optional least-squares refinement
-# =============================================================================
-
-def stage_variables_to_vector(state: Dict[str, Any], active_joint_vars: List[str]) -> np.ndarray:
- try:
- from scipy.spatial.transform import Rotation
- root_rvec = Rotation.from_matrix(state['root_R']).as_rotvec()
- except Exception:
- root_rvec = np.zeros(3, dtype=np.float64)
-
- root_t = np.asarray(state['root_t'], dtype=np.float64).reshape(3)
- values = list(root_t) + list(np.asarray(root_rvec, dtype=np.float64))
- for var in active_joint_vars:
- values.append(float(state['joint_values'].get(var, 0.0)))
- return np.asarray(values, dtype=np.float64)
-
-
-def vector_to_state(x: np.ndarray, template_state: Dict[str, Any], active_joint_vars: List[str]) -> Dict[str, Any]:
- try:
- from scipy.spatial.transform import Rotation
- except Exception:
- Rotation = None
-
- x = np.asarray(x, dtype=np.float64).reshape(-1)
- out = {
- 'root_t': x[0:3].copy(),
- 'root_R': template_state['root_R'].copy(),
- 'joint_values': dict(template_state['joint_values']),
- }
-
- root_rvec = x[3:6].copy()
- if Rotation is not None:
- out['root_R'] = Rotation.from_rotvec(root_rvec).as_matrix()
- else:
- angle = safe_norm(root_rvec)
- out['root_R'] = np.eye(3, dtype=np.float64) if angle < 1e-12 else axis_angle_rotation(root_rvec / angle, angle)
-
- idx = 6
- for var in active_joint_vars:
- if idx >= len(x):
- break
- out['joint_values'][var] = float(x[idx])
- idx += 1
- return out
-
-
-def residuals_stage(
- x: np.ndarray,
- link_infos: Dict[str, LinkInfo],
- observed_markers: Dict[int, Dict[str, Any]],
- template_state: Dict[str, Any],
- active_joint_vars: List[str],
- active_links: set[str],
- cfg: EstimationConfig,
-) -> np.ndarray:
- state = vector_to_state(x, template_state, active_joint_vars)
- pred = predict_marker_positions(link_infos, state, active_links=active_links)
- res: List[float] = []
-
- for marker_id, obs in observed_markers.items():
- if marker_id not in pred:
- continue
- link_name = obs.get('link', None)
- marker = None
- if link_name is not None and link_name in link_infos:
- marker = next((m for m in link_infos[link_name].markers if m.marker_id == marker_id), None)
- w = marker_weight(marker, base_weight=cfg.marker_base_weight) if marker is not None else cfg.marker_base_weight
- sqrt_w = math.sqrt(max(1e-12, w))
- p_obs = np.asarray(obs['position_m'], dtype=np.float64)
- p_pred = pred[marker_id]
- d = (p_pred - p_obs) * sqrt_w
- res.extend(d.tolist())
-
- if cfg.root_pose_prior_weight > 0:
- root_t_prior = np.asarray(template_state['root_t'], dtype=np.float64)
- try:
- from scipy.spatial.transform import Rotation
- root_rvec_prior = Rotation.from_matrix(template_state['root_R']).as_rotvec()
- except Exception:
- root_rvec_prior = np.zeros(3, dtype=np.float64)
- w = math.sqrt(max(1e-12, cfg.root_pose_prior_weight))
- res.extend(((x[0:3] - root_t_prior) * w).tolist())
- res.extend(((x[3:6] - root_rvec_prior) * w).tolist())
-
- if cfg.joint_prior_weight > 0:
- idx = 6
- w = math.sqrt(max(1e-12, cfg.joint_prior_weight))
- for var in active_joint_vars:
- if idx >= len(x):
- break
- q = x[idx]
- q0 = template_state['joint_values'].get(var, 0.0)
- dq = wrap_angle_rad(float(q - q0)) if abs(q0) <= math.pi * 2.5 else float(q - q0)
- res.append(dq * w)
- idx += 1
-
- return np.asarray(res, dtype=np.float64)
-
-
-def optional_solver_refine(
- link_infos: Dict[str, LinkInfo],
- observed_markers: Dict[int, Dict[str, Any]],
- state: Dict[str, Any],
- active_links: List[str],
- cfg: EstimationConfig,
-) -> Tuple[Dict[str, Any], Dict[str, Any]]:
- try:
- from scipy.optimize import least_squares
- except Exception:
- return state, {'success': False, 'message': 'scipy unavailable'}
-
- active_joint_vars = [link_infos[ln].joint_variable for ln in active_links if link_infos[ln].joint_variable is not None]
- x0 = stage_variables_to_vector(state, active_joint_vars)
- active_set = set(active_links)
-
- result = least_squares(
- lambda x: residuals_stage(x, link_infos, observed_markers, state, active_joint_vars, active_set, cfg),
- x0,
- loss=cfg.robust_loss,
- f_scale=1.0,
- max_nfev=cfg.max_iterations,
- verbose=1,
- )
- refined = vector_to_state(result.x, state, active_joint_vars)
- info = {
- 'success': bool(result.success),
- 'status': int(result.status),
- 'message': str(result.message),
- 'nfev': int(result.nfev),
- 'cost': float(np.sum(result.fun ** 2)),
- }
- return refined, info
-
-
-# =============================================================================
-# Sequential estimation driver
-# =============================================================================
-
-def sequential_geometric_estimation(
- link_infos: Dict[str, LinkInfo],
- observed_markers: Dict[int, Dict[str, Any]],
- params: StatePoseParams,
- cfg: EstimationConfig,
-) -> Tuple[Dict[str, Any], List[StageResult]]:
- ordered = topological_links(link_infos)
- if not ordered:
- return build_state_template(link_infos), []
-
- start_n = max(1, min(int(params.numbers_of_elements_to_consider_start), len(ordered)))
- final_n = max(start_n, min(int(params.numbers_of_elements_to_consider_final), len(ordered)))
-
- state = build_state_template(link_infos)
- stage_results: List[StageResult] = []
-
- for n in range(start_n, final_n + 1):
- active_links = ordered[:n]
- stage_state, stage_info = optimize_geometric_prefix(link_infos, observed_markers, state, active_links, params)
- state = stage_state
-
- if params.solver_in_between_geometrical:
- state, solver_info = optional_solver_refine(link_infos, observed_markers, state, active_links, cfg)
- stage_info['solver_in_between'] = solver_info
-
- stage_obs = active_observations_for_links(observed_markers, set(active_links))
- marker_reports, stats = marker_residual_error_for_state(link_infos, state, stage_obs, active_links=set(active_links))
-
- active_joint_vars = [link_infos[ln].joint_variable for ln in active_links if link_infos[ln].joint_variable is not None]
- stage_result = StageResult(
- stage_idx=len(stage_results),
- active_links=active_links,
- active_joint_vars=active_joint_vars,
- num_markers_used=stats['num_markers_used'],
- mean_error_m=stats['mean_error_m'],
- rms_error_m=stats['rms_error_m'],
- worst_error_m=stats['worst_error_m'],
- method='geometric_prefix' + ('+solver' if params.solver_in_between_geometrical else ''),
- optimizer_info=stage_info,
- )
- stage_results.append(stage_result)
-
- if cfg.show_stage_reports:
- print(
- f"[INFO] Stage {stage_result.stage_idx} | links={len(active_links)} | joints={len(active_joint_vars)} | "
- f"markers={stage_result.num_markers_used} | mean={None if stats['mean_error_m'] is None else stats['mean_error_m']*1000.0:.2f} mm"
- )
-
- if params.solver_after_geometrical:
- state, solver_info = optional_solver_refine(link_infos, observed_markers, state, ordered[:final_n], cfg)
- if stage_results:
- stage_results[-1].optimizer_info['solver_after_geometrical'] = solver_info
-
- return state, stage_results
-
-
-# =============================================================================
-# Output
-# =============================================================================
-
-def state_to_json(
- robot_data: Dict[str, Any],
- link_infos: Dict[str, LinkInfo],
- state: Dict[str, Any],
- observed_markers: Dict[int, Dict[str, Any]],
- marker_reports: List[Dict[str, Any]],
- stats: Dict[str, Any],
- input_file: str,
- robot_file: str,
- stage_results: List[StageResult],
- params: StatePoseParams,
-) -> Dict[str, Any]:
- movements = {}
- for link_name in topological_links(link_infos):
- link = link_infos[link_name]
- if not link.joint_variable:
- continue
-
- q = float(state['joint_values'].get(link.joint_variable, 0.0))
- jt = (link.joint_type or '').strip().lower()
- entry = {
- 'joint_type': jt,
- 'link': link_name,
- 'estimated': True,
- }
- if jt == 'revolute':
- entry['value_rad'] = q
- entry['value_deg'] = float(math.degrees(q))
- elif jt == 'linear':
- entry['value_m'] = q
- entry['value_mm'] = q * 1000.0
- else:
- entry['value'] = q
- movements[link.joint_variable] = entry
-
- link_pose_entries = []
- cache: Dict[Tuple[str, Tuple[Tuple[str, float], ...]], np.ndarray] = {}
- for link_name in topological_links(link_infos):
- T = world_to_link_transform(link_name, link_infos, state, cache)
- R = T[:3, :3]
- t = T[:3, 3]
- link = link_infos[link_name]
- num_used = sum(1 for m in link.markers if m.marker_id in observed_markers)
- link_pose_entries.append({
- 'link': link_name,
- 'parent': link.parent,
- 'position_m': [float(x) for x in t],
- 'rotation_matrix': [[float(v) for v in row] for row in R],
- 'euler_xyz_deg': [float(x) for x in matrix_to_euler_xyz(R)],
- 'num_observed_markers': int(num_used),
- 'num_markers_total': int(len(link.markers)),
- })
-
- root_link = next((name for name, info in link_infos.items() if info.parent is None), None)
- root_T = make_transform(state['root_R'], state['root_t'])
-
- out = {
- 'schema_version': '3.0',
- 'created_utc': time.strftime('%Y-%m-%dT%H:%M:%SZ', time.gmtime()),
- 'source': {
- 'marker_positions_file': input_file,
- 'robot_file': robot_file,
- },
- 'state_pose_params': {
- 'numbers_of_Elements_to_consider_start': params.numbers_of_elements_to_consider_start,
- 'numbers_of_Elements_to_consider_final': params.numbers_of_elements_to_consider_final,
- 'solver_in_between_geometrical': params.solver_in_between_geometrical,
- 'solver_after_geometrical': params.solver_after_geometrical,
- 'geometric_passes_per_stage': params.geometric_passes_per_stage,
- 'revolute_search_coarse_deg': params.revolute_search_coarse_deg,
- 'revolute_search_fine_deg': params.revolute_search_fine_deg,
- 'root_pose_min_markers': params.root_pose_min_markers,
- },
- 'summary': {
- 'num_links': int(len(link_infos)),
- 'num_observed_markers': int(len(observed_markers)),
- 'num_link_markers': int(sum(len(li.markers) for li in link_infos.values())),
- 'root_link': root_link,
- 'fit_stats': stats,
- 'optimizer': state.get('_optimizer', {}),
- 'stages': [
- {
- 'stage_idx': int(s.stage_idx),
- 'active_links': s.active_links,
- 'active_joint_vars': s.active_joint_vars,
- 'num_markers_used': int(s.num_markers_used),
- 'mean_error_m': s.mean_error_m,
- 'rms_error_m': s.rms_error_m,
- 'worst_error_m': s.worst_error_m,
- 'method': s.method,
- 'optimizer_info': s.optimizer_info,
- }
- for s in stage_results
- ],
- },
- 'world_pose': {
- 'root_translation_m': [float(x) for x in root_T[:3, 3]],
- 'root_rotation_matrix': [[float(v) for v in row] for row in root_T[:3, :3]],
- 'root_euler_xyz_deg': [float(x) for x in matrix_to_euler_xyz(root_T[:3, :3])],
- },
- 'movements': movements,
- 'links': link_pose_entries,
- 'markers': marker_reports,
- }
- return out
-
-
-# =============================================================================
-# Main
-# =============================================================================
-
-def main() -> None:
- parser = argparse.ArgumentParser(
- description='Sequential geometric robot-state estimation from optimized ArUco marker positions'
- )
- parser.add_argument('optimized_markers', help='aruco_positions_optimized*.json')
- parser.add_argument('-robot', '--robot', required=True, help='robot.json')
- parser.add_argument('-out', '--out', default=None, help='Output robot_state.json path')
- parser.add_argument('--maxIterations', type=int, default=120, help='Maximum least-squares iterations per optional refinement stage')
- parser.add_argument('--jointPriorWeight', type=float, default=0.0, help='Prior weight for joint variables during optional solver refinement')
- parser.add_argument('--rootPosePriorWeight', type=float, default=0.0, help='Prior weight for root pose during optional solver refinement')
- parser.add_argument('--markerBaseWeight', type=float, default=1.0, help='Base weight for marker residuals')
- parser.add_argument('--noSequential', action='store_true', help='Disable sequential prefix mode and run one prefix stage only')
- parser.add_argument('--forceSolverAfter', action='store_true', help='Force solver after the geometric prefix regardless of robot.json')
- parser.add_argument('--forceSolverBetween', action='store_true', help='Force solver between geometric stages regardless of robot.json')
-
- args = parser.parse_args()
-
- print('[STEP 1] Load robot.json and optimized marker positions...')
- robot_data = load_json(args.robot)
- link_infos, marker_by_id, issues = parse_robot(robot_data)
- observed_map = load_observed_markers(args.optimized_markers)
- params = load_state_pose_params(robot_data)
-
- if args.noSequential:
- params.numbers_of_elements_to_consider_start = len(topological_links(link_infos))
- params.numbers_of_elements_to_consider_final = len(topological_links(link_infos))
-
- if args.forceSolverBetween:
- params.solver_in_between_geometrical = True
- if args.forceSolverAfter:
- params.solver_after_geometrical = True
-
- for issue in issues:
- print(issue)
-
- print(f"[INFO] Links: {len(link_infos)}")
- print(f"[INFO] Known robot markers: {len(marker_by_id)}")
- print(f"[INFO] Observed optimized markers: {len(observed_map)}")
- print(f"[INFO] state_pose_params: start={params.numbers_of_elements_to_consider_start}, final={params.numbers_of_elements_to_consider_final}, between={params.solver_in_between_geometrical}, after={params.solver_after_geometrical}")
-
- cfg = EstimationConfig(
- marker_base_weight=float(args.markerBaseWeight),
- root_pose_prior_weight=float(args.rootPosePriorWeight),
- joint_prior_weight=float(args.jointPriorWeight),
- robust_loss='soft_l1',
- max_iterations=int(args.maxIterations),
- show_stage_reports=True,
- use_geometric_prefix=True,
- )
-
- print('\n[STEP 2] Geometric sequential pose estimation...')
- final_state, stage_results = sequential_geometric_estimation(
- link_infos=link_infos,
- observed_markers=observed_map,
- params=params,
- cfg=cfg,
- )
-
- print('\n[STEP 3] Build report and save robot_state.json...')
- marker_reports, stats = marker_residual_error_for_state(link_infos, final_state, observed_map)
-
- out_data = state_to_json(
- robot_data=robot_data,
- link_infos=link_infos,
- state=final_state,
- observed_markers=observed_map,
- marker_reports=marker_reports,
- stats=stats,
- input_file=args.optimized_markers,
- robot_file=args.robot,
- stage_results=stage_results,
- params=params,
- )
-
- out_path = args.out
- if out_path is None:
- out_dir = os.path.dirname(args.optimized_markers) or '.'
- out_path = os.path.join(out_dir, 'robot_state.json')
-
- save_json(out_path, out_data)
-
- print(f"[INFO] Saved to {out_path}")
- if stats['mean_error_m'] is not None:
- print(f"[INFO] Mean marker error: {stats['mean_error_m'] * 1000.0:.2f} mm")
- print(f"[INFO] RMS marker error : {stats['rms_error_m'] * 1000.0:.2f} mm")
- print(f"[INFO] Worst marker error: {stats['worst_error_m'] * 1000.0:.2f} mm")
- else:
- print('[INFO] No marker error statistics available.')
-
-
-if __name__ == '__main__':
- main()
diff --git a/pipeline/4_robotState_estimation_v7.py b/pipeline/4_robotState_estimation_v7.py
deleted file mode 100644
index 1e8dc83..0000000
--- a/pipeline/4_robotState_estimation_v7.py
+++ /dev/null
@@ -1,484 +0,0 @@
-#!/usr/bin/env python3
-"""
-4_robotState_estimation_v7.py
-
-Estimate the Arm1 joint angle from ArUco marker positions.
-
-What this script does
----------------------
-1. Loads a robot configuration JSON and extracts the local marker positions of Arm1.
-2. Loads an observation JSON with marker positions (e.g. aruco_positions_optimized.json).
-3. Matches observed markers to Arm1 markers by marker_id.
-4. Estimates the Arm1 angle in two ways:
-
- Option 1 (single marker, optional):
- ----------------------------------
- If a world-space joint origin is known, estimate angle from one marker by comparing
- the vector from joint origin -> marker in the model and in the observation.
-
- Option 2 (marker pairs, default and robust):
- ------------------------------------------
- For every pair of Arm1 markers, estimate the rotation angle from the relative vector
- between the two markers. This is translation-invariant and usually the preferred method.
-
-5. Aggregates all valid estimates using a weighted circular mean and reports:
- mean angle, circular variance, circular std, linear std on wrapped samples, and per-estimate details.
-
-Coordinate conventions
-----------------------
-The provided robot config says:
-- right-handed coordinate system
-- x right, y backward, z up
-
-Arm1 rotates around the x-axis (joint axis [-1, 0, 0] in the supplied config).
-The script therefore works in 3D and automatically respects the joint axis sign.
-
-Usage
------
- python 4_robotState_estimation_v7.py \
- --robot-json robot_running_2026_05_30.json \
- --aruco-json aruco_positions_optimized.json
-
-Optional:
- --joint-origin-world X Y Z # enables single-marker estimates
- --output-json result.json
- --arm-link Arm1
-
-The script prints a concise report and can also write a JSON summary.
-
-Notes
------
-- This script is intentionally defensive: it accepts positions in meters or millimeters.
-- It does not require any additional robot state beyond the Arm1 marker geometry.
-- If only a subset of Arm1 markers is observed, the pairwise estimator still works.
-"""
-
-from __future__ import annotations
-
-import argparse
-import json
-import math
-from dataclasses import dataclass, asdict
-from itertools import combinations
-from pathlib import Path
-from typing import Dict, Iterable, List, Optional, Sequence, Tuple
-
-import numpy as np
-
-
-# ----------------------------
-# Data structures
-# ----------------------------
-
-@dataclass
-class MarkerSpec:
- marker_id: int
- local_position_mm: np.ndarray
-
-
-@dataclass
-class MarkerObservation:
- marker_id: int
- position_mm: np.ndarray
- link: Optional[str] = None
-
-
-@dataclass
-class AngleEstimate:
- source: str
- angle_rad: float
- angle_deg: float
- weight: float
- marker_ids: Tuple[int, ...]
-
-
-# ----------------------------
-# Helpers
-# ----------------------------
-
-def _as_np3(value: Sequence[float]) -> np.ndarray:
- arr = np.asarray(value, dtype=float).reshape(3)
- return arr
-
-
-def load_json(path: Path) -> dict:
- with path.open("r", encoding="utf-8") as f:
- return json.load(f)
-
-
-def unit_scale_to_mm(units: Optional[str]) -> float:
- if not units:
- return 1.0
- u = str(units).strip().lower()
- if u in {"mm", "millimeter", "millimeters"}:
- return 1.0
- if u in {"m", "meter", "meters"}:
- return 1000.0
- if u in {"cm", "centimeter", "centimeters"}:
- return 10.0
- # conservative default
- return 1.0
-
-
-def normalize_angle_rad(angle: float) -> float:
- """Wrap to (-pi, pi]."""
- return (angle + math.pi) % (2 * math.pi) - math.pi
-
-
-def angle_between_vectors(v_from: np.ndarray, v_to: np.ndarray, axis: np.ndarray) -> float:
- """
- Signed angle rotating v_from onto v_to around axis.
-
- Uses atan2( axis_hat · (v_from × v_to), v_from · v_to ).
- """
- a = np.asarray(axis, dtype=float).reshape(3)
- an = np.linalg.norm(a)
- if an == 0:
- raise ValueError("Rotation axis must be non-zero.")
- a = a / an
-
- v1 = np.asarray(v_from, dtype=float).reshape(3)
- v2 = np.asarray(v_to, dtype=float).reshape(3)
-
- n1 = np.linalg.norm(v1)
- n2 = np.linalg.norm(v2)
- if n1 == 0 or n2 == 0:
- raise ValueError("Cannot compute angle for zero-length vector.")
- cross_term = float(np.dot(a, np.cross(v1, v2)))
- dot_term = float(np.dot(v1, v2))
- return math.atan2(cross_term, dot_term)
-
-
-def weighted_circular_mean(angles_rad: Sequence[float], weights: Sequence[float]) -> Tuple[float, float, float]:
- """
- Returns:
- mean_angle_rad, circular_variance, circular_std_rad
-
- circular_variance = 1 - R, where R is the normalized resultant length.
- circular_std_rad = sqrt(-2 ln R) if R>0 else pi.
- """
- if len(angles_rad) == 0:
- raise ValueError("No angles provided.")
-
- ang = np.asarray(angles_rad, dtype=float)
- w = np.asarray(weights, dtype=float)
- w = np.clip(w, 0.0, None)
-
- if np.sum(w) <= 0:
- w = np.ones_like(w)
-
- s = np.sum(w * np.sin(ang))
- c = np.sum(w * np.cos(ang))
- mean = math.atan2(s, c)
-
- R = math.sqrt(s * s + c * c) / float(np.sum(w))
- R = min(max(R, 0.0), 1.0)
-
- circular_variance = 1.0 - R
- circular_std = math.sqrt(max(0.0, -2.0 * math.log(max(R, 1e-15)))) if R > 0 else math.pi
- return mean, circular_variance, circular_std
-
-
-def weighted_linear_std_rad(angles_rad: Sequence[float], mean_rad: float, weights: Sequence[float]) -> float:
- if len(angles_rad) == 0:
- return float("nan")
- ang = np.asarray([normalize_angle_rad(a - mean_rad) for a in angles_rad], dtype=float)
- w = np.asarray(weights, dtype=float)
- w = np.clip(w, 0.0, None)
- if np.sum(w) <= 0:
- w = np.ones_like(w)
- mu = np.sum(w * ang) / np.sum(w)
- var = np.sum(w * (ang - mu) ** 2) / np.sum(w)
- return float(math.sqrt(max(0.0, var)))
-
-
-def extract_arm_markers(robot: dict, arm_link_name: str) -> Tuple[np.ndarray, List[MarkerSpec], np.ndarray]:
- """
- Returns:
- joint_origin_mm, markers, joint_axis
- """
- links = robot.get("links", {})
- if arm_link_name not in links:
- # case-insensitive fallback
- lookup = {k.lower(): k for k in links.keys()}
- if arm_link_name.lower() not in lookup:
- raise KeyError(f"Link '{arm_link_name}' not found in robot.json.")
- arm_link_name = lookup[arm_link_name.lower()]
-
- arm = links[arm_link_name]
- jt = arm.get("jointToParent", {})
- joint_origin_mm = _as_np3(jt.get("origin", [0, 0, 0]))
- joint_axis = _as_np3(jt.get("axis", [1, 0, 0]))
-
- markers = []
- for m in arm.get("markers", []):
- if "id" not in m or "position" not in m:
- continue
- markers.append(MarkerSpec(marker_id=int(m["id"]), local_position_mm=_as_np3(m["position"])))
-
- if not markers:
- raise ValueError(f"No markers found on link '{arm_link_name}'.")
-
- return joint_origin_mm, markers, joint_axis
-
-
-def load_observations(observations_json: dict) -> List[MarkerObservation]:
- markers = observations_json.get("markers", [])
- units = observations_json.get("units", {})
- scale = 1.0
- if isinstance(units, dict):
- scale = unit_scale_to_mm(units.get("length"))
- else:
- scale = unit_scale_to_mm(units)
-
- obs: List[MarkerObservation] = []
- for m in markers:
- if "marker_id" not in m and "id" not in m:
- continue
- marker_id = int(m.get("marker_id", m.get("id")))
- if "position_mm" in m:
- pos = _as_np3(m["position_mm"])
- elif "position_m" in m:
- pos = _as_np3(m["position_m"]) * 1000.0
- elif "position" in m:
- pos = _as_np3(m["position"]) * scale
- else:
- continue
- obs.append(MarkerObservation(marker_id=marker_id, position_mm=pos, link=m.get("link")))
- return obs
-
-
-def match_markers(specs: Sequence[MarkerSpec], observations: Sequence[MarkerObservation]) -> Dict[int, Tuple[np.ndarray, np.ndarray]]:
- """
- Returns marker_id -> (local_position_mm, observed_position_mm)
- """
- spec_map = {m.marker_id: m.local_position_mm for m in specs}
- out = {}
- for o in observations:
- if o.marker_id in spec_map:
- out[o.marker_id] = (spec_map[o.marker_id], o.position_mm)
- return out
-
-
-def estimate_pairwise_angles(
- matched: Dict[int, Tuple[np.ndarray, np.ndarray]],
- axis: np.ndarray,
- min_baseline_mm: float = 1.0,
-) -> List[AngleEstimate]:
- estimates: List[AngleEstimate] = []
- ids = sorted(matched.keys())
-
- for id1, id2 in combinations(ids, 2):
- p1_local, p1_obs = matched[id1]
- p2_local, p2_obs = matched[id2]
-
- v_local = p2_local - p1_local
- v_obs = p2_obs - p1_obs
-
- baseline_local = float(np.linalg.norm(v_local))
- baseline_obs = float(np.linalg.norm(v_obs))
- if baseline_local < min_baseline_mm or baseline_obs < min_baseline_mm:
- continue
-
- try:
- angle = angle_between_vectors(v_local, v_obs, axis)
- except ValueError:
- continue
-
- # Weight long baselines a little more strongly; if a pair is very short, it becomes noisy.
- weight = max(1e-6, baseline_local * baseline_obs)
- estimates.append(
- AngleEstimate(
- source="pair",
- angle_rad=normalize_angle_rad(angle),
- angle_deg=math.degrees(normalize_angle_rad(angle)),
- weight=weight,
- marker_ids=(id1, id2),
- )
- )
- return estimates
-
-
-def estimate_single_marker_angles(
- matched: Dict[int, Tuple[np.ndarray, np.ndarray]],
- joint_origin_world_mm: np.ndarray,
- axis: np.ndarray,
-) -> List[AngleEstimate]:
- """
- Single-marker estimates require a known world-space joint origin.
-
- For each marker, compare:
- v_local = marker_local - joint_origin_local
- v_obs = marker_obs - joint_origin_world
-
- In the provided Arm1 config the joint origin is the local origin of Arm1, so
- joint_origin_local is taken as [0,0,0].
- """
- estimates: List[AngleEstimate] = []
- joint_origin_world_mm = _as_np3(joint_origin_world_mm)
-
- for marker_id, (p_local, p_obs) in matched.items():
- v_local = p_local # Arm1 local origin is at the joint
- v_obs = p_obs - joint_origin_world_mm
-
- if np.linalg.norm(v_local) < 1e-9 or np.linalg.norm(v_obs) < 1e-9:
- continue
-
- try:
- angle = angle_between_vectors(v_local, v_obs, axis)
- except ValueError:
- continue
-
- weight = float(np.linalg.norm(v_local))
- estimates.append(
- AngleEstimate(
- source="single",
- angle_rad=normalize_angle_rad(angle),
- angle_deg=math.degrees(normalize_angle_rad(angle)),
- weight=weight,
- marker_ids=(marker_id,),
- )
- )
- return estimates
-
-
-def build_report(
- arm_link_name: str,
- joint_origin_mm: np.ndarray,
- joint_axis: np.ndarray,
- matched: Dict[int, Tuple[np.ndarray, np.ndarray]],
- estimates: List[AngleEstimate],
-) -> dict:
- if not estimates:
- raise ValueError("No valid angle estimates could be computed.")
-
- mean_rad, circ_var, circ_std_rad = weighted_circular_mean(
- [e.angle_rad for e in estimates],
- [e.weight for e in estimates],
- )
- lin_std_rad = weighted_linear_std_rad(
- [e.angle_rad for e in estimates],
- mean_rad,
- [e.weight for e in estimates],
- )
-
- # unwrap around mean for a stable arithmetic mean/std reporting
- unwrapped_deg = []
- for e in estimates:
- d = math.degrees(normalize_angle_rad(e.angle_rad - mean_rad))
- unwrapped_deg.append(d)
-
- report = {
- "link": arm_link_name,
- "joint_origin_mm": joint_origin_mm.tolist(),
- "joint_axis": joint_axis.tolist(),
- "num_matched_markers": len(matched),
- "num_estimates": len(estimates),
- "mean_angle_rad": mean_rad,
- "mean_angle_deg": math.degrees(mean_rad),
- "circular_variance": circ_var,
- "circular_std_rad": circ_std_rad,
- "circular_std_deg": math.degrees(circ_std_rad),
- "linear_std_rad": lin_std_rad,
- "linear_std_deg": math.degrees(lin_std_rad),
- "estimate_spread_deg_unwrapped": {
- "min": float(np.min(unwrapped_deg)) if unwrapped_deg else None,
- "max": float(np.max(unwrapped_deg)) if unwrapped_deg else None,
- "mean_abs": float(np.mean(np.abs(unwrapped_deg))) if unwrapped_deg else None,
- },
- "estimates": [asdict(e) for e in estimates],
- }
- return report
-
-
-def pretty_print_report(report: dict) -> None:
- print("\nArm angle estimation report")
- print("=" * 32)
- print(f"Link : {report['link']}")
- print(f"Matched markers : {report['num_matched_markers']}")
- print(f"Valid estimates : {report['num_estimates']}")
- print(f"Mean angle : {report['mean_angle_deg']:.3f} deg")
- print(f"Circular variance : {report['circular_variance']:.6f}")
- print(f"Circular std : {report['circular_std_deg']:.3f} deg")
- print(f"Linear std (wrapped): {report['linear_std_deg']:.3f} deg")
-
- print("\nIndividual estimates")
- print("-" * 32)
- for e in report["estimates"]:
- mids = ",".join(str(x) for x in e["marker_ids"])
- print(f"{e['source']:>6s} markers [{mids:<8s}] angle={e['angle_deg']:>8.3f} deg weight={e['weight']:.3f}")
-
-
-def estimate_arm1_state(
- robot_json: dict,
- observations_json: dict,
- arm_link_name: str = "Arm1",
- joint_origin_world_mm: Optional[Sequence[float]] = None,
-) -> dict:
- joint_origin_mm, arm_markers, joint_axis = extract_arm_markers(robot_json, arm_link_name)
- observations = load_observations(observations_json)
- matched = match_markers(arm_markers, observations)
-
- if not matched:
- available = sorted(m.marker_id for m in arm_markers)
- observed = sorted(o.marker_id for o in observations)
- raise ValueError(
- f"No Arm1 marker matches found. Arm markers: {available}. Observed IDs present in file: {observed[:25]}"
- + ("..." if len(observed) > 25 else "")
- )
-
- estimates = estimate_pairwise_angles(matched, axis=joint_axis)
-
- # Optional single-marker estimates only if a world-space joint origin is supplied.
- if joint_origin_world_mm is not None:
- estimates.extend(estimate_single_marker_angles(matched, _as_np3(joint_origin_world_mm), axis=joint_axis))
-
- report = build_report(
- arm_link_name=arm_link_name,
- joint_origin_mm=joint_origin_mm,
- joint_axis=joint_axis,
- matched=matched,
- estimates=estimates,
- )
- return report
-
-
-def main() -> int:
- parser = argparse.ArgumentParser(description="Estimate Arm1 angle from ArUco marker positions.")
- parser.add_argument("--robot-json", type=Path, default=Path("robot.json"), help="Path to robot config JSON.")
- parser.add_argument("--aruco-json", type=Path, default=Path("aruco_positions_optimized.json"), help="Path to marker observation JSON.")
- parser.add_argument("--arm-link", type=str, default="Arm1", help="Name of the link to estimate.")
- parser.add_argument(
- "--joint-origin-world",
- type=float,
- nargs=3,
- default=None,
- metavar=("X", "Y", "Z"),
- help="Optional world-space joint origin for single-marker estimates.",
- )
- parser.add_argument("--output-json", type=Path, default=None, help="Optional path to write the result JSON.")
- args = parser.parse_args()
-
- robot_json = load_json(args.robot_json)
- observations_json = load_json(args.aruco_json)
-
- report = estimate_arm1_state(
- robot_json=robot_json,
- observations_json=observations_json,
- arm_link_name=args.arm_link,
- joint_origin_world_mm=args.joint_origin_world,
- )
-
- pretty_print_report(report)
-
- if args.output_json:
- args.output_json.parent.mkdir(parents=True, exist_ok=True)
- with args.output_json.open("w", encoding="utf-8") as f:
- json.dump(report, f, indent=2, ensure_ascii=False)
- print(f"\nWrote JSON summary to: {args.output_json}")
-
- return 0
-
-
-if __name__ == "__main__":
- raise SystemExit(main())
diff --git a/pipeline/4_v8_4a_base_slider.py b/pipeline/4_v8_4a_base_slider.py
deleted file mode 100644
index 7720f74..0000000
--- a/pipeline/4_v8_4a_base_slider.py
+++ /dev/null
@@ -1,207 +0,0 @@
-#!/usr/bin/env python3
-"""
-4a_base_slider.py
------------------
-Estimate the Base linear-slider position (variable 'x', in mm) from
-observed world-space marker positions produced by step 3.
-
-Mathematical principle
-----------------------
-The Base slides along the world x-axis.
-Every joint between Base and the observable arm markers rotates around
-the x-axis (axis ≈ [-1,0,0] for Arm1, Ellbow, Arm2).
-
-Rotation around the x-axis NEVER changes the x-coordinate of any point.
-Therefore, for every marker on Arm1, Ellbow, or Arm2:
-
- world_x = x_slider + link_frame_x_at_zero + local_x
-
-where link_frame_x_at_zero is the x of the link-frame origin in world
-when all joint variables are zero (constant, readable from robot.json).
-
-Rearranging:
- x_slider = world_x - link_frame_x_at_zero - local_x
-
-This holds for ANY rotation angle y, z, a of the arm links.
-
-This gives one independent estimate per observed marker.
-We report their mean (and statistics) as the final estimate.
-
-Usage
------
- python 4a_base_slider.py --robot robot.json --aruco aruco_positions_optimized.json
-
- python 4a_base_slider.py --robot robot.json --aruco aruco_positions_optimized.json \\
- --links Arm1 Ellbow Arm2 --output 4a_result.json
-"""
-
-from __future__ import annotations
-
-import argparse
-import json
-import math
-from pathlib import Path
-from typing import Dict, List, Optional
-
-import numpy as np
-from robot_fk import RobotFK
-
-
-# ──────────────────────────────────────────────────────────────
-# I/O helpers
-# ──────────────────────────────────────────────────────────────
-
-def _load_json(path: Path) -> dict:
- with path.open("r", encoding="utf-8") as f:
- return json.load(f)
-
-
-def _load_observed_mm(aruco_json: dict) -> Dict[int, np.ndarray]:
- """marker_id → world position in mm, from aruco_positions_optimized.json."""
- result: Dict[int, np.ndarray] = {}
- for m in aruco_json.get("markers", []):
- mid = int(m.get("marker_id", m.get("id", -1)))
- if mid < 0:
- continue
- if "position_mm" in m:
- result[mid] = np.array(m["position_mm"], dtype=float)
- elif "position_m" in m:
- result[mid] = np.array(m["position_m"], dtype=float) * 1000.0
- return result
-
-
-# ──────────────────────────────────────────────────────────────
-# Core estimator
-# ──────────────────────────────────────────────────────────────
-
-def estimate_base_slider(
- fk: RobotFK,
- observed_mm: Dict[int, np.ndarray],
- use_links: Optional[List[str]] = None,
- verbose: bool = True,
-) -> dict:
- """
- Parameters
- ----------
- fk : RobotFK instance
- observed_mm : dict marker_id -> world position [mm]
- use_links : links whose markers are used (default: Arm1, Ellbow, Arm2)
- All must be connected to Base via only x-axis rotations.
- verbose : print breakdown
-
- Returns
- -------
- dict with keys:
- status, x_mean_mm, x_median_mm, x_std_mm,
- num_markers, per_marker
- """
- if use_links is None:
- use_links = ["Arm1", "Ellbow", "Arm2"]
-
- # Pre-compute link_frame_x at zero-state for each target link
- link_x0: Dict[str, float] = {}
- for lname in use_links:
- if lname in fk.links:
- link_x0[lname] = fk.link_x_at_zero_state(lname)
-
- estimates: List[float] = []
- per_marker: List[dict] = []
-
- for lname in use_links:
- if lname not in fk.links or lname not in link_x0:
- continue
- x0 = link_x0[lname]
-
- for m in fk.links[lname].get("markers", []):
- mid = int(m.get("id", -1))
- if mid < 0 or "position" not in m:
- continue
- if mid not in observed_mm:
- continue # marker not seen in this pose
-
- local_x = float(m["position"][0])
- world_x = float(observed_mm[mid][0])
- x_est = world_x - x0 - local_x
-
- estimates.append(x_est)
- per_marker.append({
- "marker_id": mid,
- "link": lname,
- "local_x_mm": local_x,
- "link_x0_mm": x0,
- "world_x_mm": world_x,
- "x_estimate_mm": x_est,
- })
-
- if not estimates:
- msg = (f"No observed markers found on links {use_links}. "
- "Check that aruco_positions_optimized.json contains "
- "markers from those links.")
- if verbose:
- print(f"[4a FAILED] {msg}")
- return {"status": "failed", "reason": msg, "use_links": use_links}
-
- arr = np.array(estimates)
- x_mean = float(np.mean(arr))
- x_median = float(np.median(arr))
- x_std = float(np.std(arr, ddof=0))
-
- if verbose:
- print("\n── 4a: Base slider (x) ──────────────────────────────────")
- print(f" Links used: {use_links}")
- print(f" Markers used: {len(estimates)}")
- print(f" x = {x_mean:+.2f} mm (median {x_median:+.2f}, σ {x_std:.2f})")
- if x_std > 5.0:
- print(f" [WARN] high spread ({x_std:.1f} mm) – step-3 errors or wrong link list")
- print(f"\n Per-marker:")
- print(f" {'ID':>5} {'Link':8} {'local_x':>8} {'world_x':>8} {'x_est':>8}")
- print(f" " + "-"*46)
- for pm in per_marker:
- print(f" {pm['marker_id']:>5} {pm['link']:8} "
- f"{pm['local_x_mm']:>8.2f} {pm['world_x_mm']:>8.2f} {pm['x_estimate_mm']:>8.2f}")
-
- return {
- "status": "ok",
- "joint": "x",
- "description": "Base linear slider",
- "x_mean_mm": x_mean,
- "x_median_mm": x_median,
- "x_std_mm": x_std,
- "num_markers": len(estimates),
- "use_links": use_links,
- "per_marker": per_marker,
- }
-
-
-# ──────────────────────────────────────────────────────────────
-# CLI
-# ──────────────────────────────────────────────────────────────
-
-def main() -> int:
- p = argparse.ArgumentParser(description="4a: estimate Base slider (x)")
- p.add_argument("--robot", type=Path, default=Path("robot.json"))
- p.add_argument("--aruco", type=Path, default=Path("aruco_positions_optimized.json"))
- p.add_argument("--links", nargs="+", default=["Arm1", "Ellbow", "Arm2"],
- dest="links",
- help="Links whose markers to use (must be x-axis-rotation chain)")
- p.add_argument("--output", type=Path, default=None,
- help="Optional JSON output path (e.g. 4a_result.json)")
- args = p.parse_args()
-
- fk = RobotFK.from_file(args.robot)
- aruco_json = _load_json(args.aruco)
- observed_mm = _load_observed_mm(aruco_json)
-
- result = estimate_base_slider(fk, observed_mm, use_links=args.links)
-
- if args.output and result["status"] == "ok":
- args.output.parent.mkdir(parents=True, exist_ok=True)
- with args.output.open("w", encoding="utf-8") as f:
- json.dump(result, f, indent=2)
- print(f"\n Saved → {args.output}")
-
- return 0 if result["status"] == "ok" else 1
-
-
-if __name__ == "__main__":
- raise SystemExit(main())
diff --git a/pipeline/4_v8_4d_direct_hand_pose.py b/pipeline/4_v8_4d_direct_hand_pose.py
deleted file mode 100644
index 81e436d..0000000
--- a/pipeline/4_v8_4d_direct_hand_pose.py
+++ /dev/null
@@ -1,464 +0,0 @@
-#!/usr/bin/env python3
-"""
-4d_direct_hand_pose.py
-----------------------
-Estimate Hand/Palm/Finger joint angles (b, c, e) directly from ArUco
-marker observations — WITHOUT relying on step-3 triangulated positions.
-
-Why this is needed
-------------------
-Step 3 triangulates marker centres from multiple cameras. For markers
-far above the board (arm extended in Z), the depth estimation from
-triangulation degrades: all reference Board markers lie in a flat plane,
-so the cameras are poorly conditioned for Z estimation of out-of-plane points.
-
-This script takes a completely different route:
-
- 1. For each camera that sees a finger or hand marker, run cv2.solvePnP
- on the four detected corners. solvePnP uses the apparent size /
- aspect ratio of the marker to recover depth — it is NOT affected
- by the flat-board reference plane problem.
-
- 2. Use the known camera-in-world pose (from step 2) to convert the
- per-camera marker pose to world frame.
-
- 3. Fuse all world-frame marker poses with quality weighting.
-
- 4. Given world-frame positions for all visible finger markers, and the
- known local positions in the FingerA/FingerB frames, solve a small
- nonlinear LS problem for (b, c, e) using scipy.
-
-What you need
--------------
- - robot.json
- - Step-2 camera-pose JSONs for all cameras
- - Step-1 aruco-detection JSONs for all cameras
- - Already-estimated x, y, z, a from 4a/4b (pass via --state-json or CLI)
-
-Usage
------
- python 4d_direct_hand_pose.py \\
- --robot robot.json \\
- --det cam1_aruco_detection.json cam2_aruco_detection.json ... \\
- --pose cam1_camera_pose.json cam2_camera_pose.json ... \\
- --state 4b_arm2_result.json \\
- --output 4d_result.json
-
-Output
-------
- {
- "b_deg": ..., "c_deg": ..., "e_mm": ...,
- "rms_mm": ...,
- "accumulated_state": {"x":..., "y":..., "z":..., "a":..., "b":..., "c":..., "e":...}
- }
-"""
-
-from __future__ import annotations
-
-import argparse
-import json
-import math
-from pathlib import Path
-from typing import Any, Dict, List, Optional, Sequence, Tuple
-
-import cv2
-import numpy as np
-
-try:
- from scipy.optimize import least_squares
- _HAS_SCIPY = True
-except ImportError:
- _HAS_SCIPY = False
-
-from robot_fk import RobotFK, STATE_KEYS
-
-
-# ──────────────────────────────────────────────────────────────
-# I/O
-# ──────────────────────────────────────────────────────────────
-
-def _load(path: Path) -> dict:
- with path.open("r", encoding="utf-8") as f:
- return json.load(f)
-
-
-def _load_state(path: Path) -> Dict[str, float]:
- raw = _load(path)
- state = raw.get("accumulated_state", raw.get("state", {}))
- return {k: float(v) for k, v in state.items() if k in STATE_KEYS}
-
-
-# ──────────────────────────────────────────────────────────────
-# ArUco / PnP helpers
-# ──────────────────────────────────────────────────────────────
-
-def _marker_local_corners(size_m: float) -> np.ndarray:
- """Four local corners of a marker (same order as OpenCV ArUco)."""
- h = size_m / 2.0
- return np.array([[-h, h, 0], [h, h, 0], [h, -h, 0], [-h, -h, 0]], dtype=np.float32)
-
-
-def _camera_pose_from_json(pose_json: dict) -> Tuple[np.ndarray, np.ndarray]:
- """
- Load world-to-camera rotation and translation from step-2 JSON.
- Returns rvec (3,), tvec (3,) — the same convention used in step 2.
- """
- wtc = pose_json["camera_pose"]["world_to_camera"]
- R = np.array(wtc["rotation_matrix"], dtype=np.float32).reshape(3, 3)
- t = np.array(wtc["translation_m"], dtype=np.float32).reshape(3)
- rvec, _ = cv2.Rodrigues(R)
- return rvec.reshape(3), t
-
-
-def _intrinsics_from_detection(det: dict) -> Tuple[np.ndarray, np.ndarray]:
- cam = det["camera"]
- K = np.array(cam["camera_matrix"], dtype=np.float32).reshape(3, 3)
- D = np.array(cam["distortion_coefficients"], dtype=np.float32).reshape(-1, 1)
- return K, D
-
-
-def _solve_marker_pose_in_camera(
- corners_px: np.ndarray,
- marker_size_m: float,
- K: np.ndarray,
- D: np.ndarray
-) -> Optional[Tuple[np.ndarray, np.ndarray]]:
- """
- Estimate marker-in-camera pose via solvePnP (IPPE_SQUARE).
- Returns (rvec_cm, tvec_cm) or None on failure.
- """
- obj = _marker_local_corners(marker_size_m)
- img = corners_px.astype(np.float32)
- ok, rvec, tvec = cv2.solvePnP(obj, img, K, D, flags=cv2.SOLVEPNP_IPPE_SQUARE)
- if not ok:
- ok, rvec, tvec = cv2.solvePnP(obj, img, K, D, flags=cv2.SOLVEPNP_ITERATIVE)
- if not ok:
- return None
- return rvec.reshape(3), tvec.reshape(3)
-
-
-def _marker_world_pose(
- rvec_cm: np.ndarray,
- tvec_cm: np.ndarray,
- rvec_wc: np.ndarray,
- tvec_wc: np.ndarray
-) -> Tuple[np.ndarray, np.ndarray]:
- """
- Convert marker-in-camera pose to marker-in-world pose.
-
- Convention (same as step 2):
- X_cam = R_wc @ X_world + t_wc
- So:
- R_wc = rvec→R(rvec_wc), camera origin in world = -R_wc^T @ t_wc
-
- Marker pose in world:
- R_mw = R_wc^T @ R_cm (world_to_marker rotation)
- t_mw = R_wc^T @ (tvec_cm - tvec_wc)
- → marker centre in world = -R_mw^T @ t_mw = R_mw^T @ (-t_mw)
-
- We return (pos_world_m, R_marker_in_world) where
- R_marker_in_world maps local marker axes to world axes.
- """
- R_wc, _ = cv2.Rodrigues(rvec_wc.reshape(3, 1))
- R_cm, _ = cv2.Rodrigues(rvec_cm.reshape(3, 1))
-
- R_cw = R_wc.T # camera-to-world rotation
-
- # Marker centre in world frame
- pos_w = R_cw @ tvec_cm.reshape(3) - R_cw @ tvec_wc.reshape(3)
-
- # Marker orientation in world frame (columns = marker x,y,z axes in world)
- R_mw = R_cw @ R_cm
-
- return pos_w.astype(np.float64), R_mw.astype(np.float64)
-
-
-# ──────────────────────────────────────────────────────────────
-# Multi-camera marker pose fusion
-# ──────────────────────────────────────────────────────────────
-
-def _observation_weight(corners_px: np.ndarray, img_w: int, img_h: int) -> float:
- """
- Simple quality weight: larger markers, more central = higher weight.
- """
- area = float(cv2.contourArea(corners_px.astype(np.float32)))
- cx, cy = corners_px.mean(axis=0)
- dx = abs(cx - img_w / 2.0) / (img_w / 2.0)
- dy = abs(cy - img_h / 2.0) / (img_h / 2.0)
- centrality = 1.0 - 0.5 * (dx + dy)
- return max(0.05, (area / 500.0) * centrality)
-
-
-def collect_marker_world_poses(
- detection_files: List[Path],
- pose_files: List[Path],
- target_marker_ids: set,
- marker_size_m: float,
- verbose: bool = True,
-) -> Dict[int, List[Dict[str, Any]]]:
- """
- For each target marker, collect world-frame pose estimates from all cameras.
-
- Returns: marker_id → list of {pos_m, R, weight, camera_id}
- """
- result: Dict[int, List[Dict[str, Any]]] = {}
-
- for det_path, pose_path in zip(detection_files, pose_files):
- det = _load(det_path)
- pose_json = _load(pose_path)
-
- K, D = _intrinsics_from_detection(det)
- rvec_wc, tvec_wc = _camera_pose_from_json(pose_json)
-
- cam_id = det.get("camera", {}).get("camera_id", str(det_path))
- img_w = int(det.get("image", {}).get("width_px", 1280))
- img_h = int(det.get("image", {}).get("height_px", 720))
-
- for d in det.get("detections", []):
- mid = int(d.get("marker_id", -1))
- if mid not in target_marker_ids:
- continue
- corners = np.array(d.get("image_points_px", []), dtype=np.float32).reshape(4, 2)
- if corners.shape != (4, 2):
- continue
-
- pnp = _solve_marker_pose_in_camera(corners, marker_size_m, K, D)
- if pnp is None:
- continue
- rvec_cm, tvec_cm = pnp
-
- pos_w, R_w = _marker_world_pose(rvec_cm, tvec_cm, rvec_wc, tvec_wc)
- w = _observation_weight(corners, img_w, img_h)
-
- result.setdefault(mid, []).append({
- "pos_m": pos_w,
- "R": R_w,
- "weight": w,
- "camera_id": cam_id,
- })
-
- if verbose:
- for mid, obs in result.items():
- print(f" M{mid}: {len(obs)} camera(s) — weights "
- f"{[f'{o[\"weight\"]:.2f}' for o in obs]}")
-
- return result
-
-
-def fuse_marker_positions(
- observations: Dict[int, List[Dict[str, Any]]]
-) -> Dict[int, np.ndarray]:
- """
- Weighted-mean fusion of world-frame marker centre positions (metres).
- """
- result: Dict[int, np.ndarray] = {}
- for mid, obs_list in observations.items():
- total_w = sum(o["weight"] for o in obs_list)
- if total_w < 1e-12:
- continue
- pos = sum(o["pos_m"] * o["weight"] for o in obs_list) / total_w
- result[mid] = pos.astype(np.float64)
- return result
-
-
-# ──────────────────────────────────────────────────────────────
-# FK-based optimizer for b, c, e
-# ──────────────────────────────────────────────────────────────
-
-def _fk_finger_positions(
- fk: RobotFK,
- base_state: Dict[str, float],
- b: float,
- c: float,
- e: float
-) -> Dict[int, np.ndarray]:
- """
- Compute world positions (m) of all FingerA and FingerB markers.
- base_state already contains x, y, z, a.
- """
- state = dict(base_state)
- state.update({"b": b, "c": c, "e": e})
- transforms = fk.compute(state)
- result = {}
- for link_name in ("FingerA", "FingerB", "Hand", "Palm"):
- if link_name not in fk.links:
- continue
- for m in fk.links[link_name].get("markers", []):
- mid = int(m.get("id", -1))
- if mid < 0 or "position" not in m:
- continue
- # position in METRES
- local_mm = np.array(m["position"], dtype=float)
- world_m = fk.marker_world(transforms, link_name, local_mm) / 1000.0
- result[mid] = world_m
- return result
-
-
-def optimize_bce(
- fk: RobotFK,
- base_state: Dict[str, float],
- observed_m: Dict[int, np.ndarray],
- b_init: float = 0.0,
- c_init: float = 0.0,
- e_init: float = 0.0,
- verbose: bool = True,
-) -> dict:
- """
- Find (b, c, e) minimising reprojection of FK finger-marker positions
- to observed world positions.
-
- Parameters
- ----------
- fk : RobotFK
- base_state : already-estimated {x, y, z, a} (all in mm/deg)
- observed_m : {marker_id: world_position_m}
- """
-
- if not _HAS_SCIPY:
- return {"status": "failed", "reason": "scipy not available (pip install scipy)"}
-
- def residuals(p):
- b, c, e = float(p[0]), float(p[1]), float(p[2])
- fk_pos = _fk_finger_positions(fk, base_state, b, c, e)
- r = []
- for mid, obs_p in observed_m.items():
- if mid in fk_pos:
- diff = fk_pos[mid] - obs_p
- r.extend(diff.tolist())
- return r
-
- # Initial residual
- r0 = residuals([b_init, c_init, e_init])
- if len(r0) < 3:
- return {
- "status": "failed",
- "reason": f"Only {len(r0)//3} finger markers matched. Need ≥1.",
- }
-
- res = least_squares(
- residuals,
- x0=[b_init, c_init, e_init],
- method="lm",
- max_nfev=2000,
- )
-
- b_est, c_est, e_est = float(res.x[0]), float(res.x[1]), float(res.x[2])
-
- rms_m = float(np.sqrt(np.mean(np.array(res.fun) ** 2)))
- rms_mm = rms_m * 1000.0
-
- accum = dict(base_state)
- accum.update({"b": b_est, "c": c_est, "e": e_est})
-
- if verbose:
- print(f"\n── 4d: Hand/Palm/Finger (b, c, e) ──────────────────────")
- print(f" Markers used: {len(observed_m)} fused positions")
- print(f" b = {b_est:+.2f} ° c = {c_est:+.2f} ° e = {e_est:+.2f} mm")
- print(f" RMS position error: {rms_mm:.2f} mm")
- if not res.success:
- print(f" [WARN] Optimizer: {res.message}")
-
- return {
- "status": "ok",
- "b_deg": b_est,
- "c_deg": c_est,
- "e_mm": e_est,
- "rms_mm": rms_mm,
- "optimizer_success": bool(res.success),
- "num_markers": len(observed_m),
- "accumulated_state": accum,
- }
-
-
-# ──────────────────────────────────────────────────────────────
-# Main
-# ──────────────────────────────────────────────────────────────
-
-def main() -> int:
- p = argparse.ArgumentParser(
- description="4d: Direct hand/finger pose from per-camera solvePnP")
- p.add_argument("--robot", type=Path, required=True)
- p.add_argument("--det", "-d", type=Path, nargs="+", required=True,
- help="*_aruco_detection.json files (one per camera)")
- p.add_argument("--pose", "-p", type=Path, nargs="+", required=True,
- help="*_camera_pose.json files (same order as --det)")
- p.add_argument("--state", type=Path, default=None,
- help="JSON from previous 4b step (accumulated_state)")
- for k in ("x", "y", "z", "a"):
- p.add_argument(f"--{k}", type=float, default=None,
- help=f"Known {k} value (overrides --state)")
- p.add_argument("--b-init", type=float, default=0.0)
- p.add_argument("--c-init", type=float, default=0.0)
- p.add_argument("--e-init", type=float, default=0.0)
- p.add_argument("--output", type=Path, default=None)
- args = p.parse_args()
-
- if len(args.det) != len(args.pose):
- print("[ERROR] --det and --pose must have the same number of files")
- return 1
-
- # Base state from file or CLI
- base_state: Dict[str, float] = {}
- if args.state:
- base_state = _load_state(args.state)
- print(f" Loaded state: {base_state}")
- for k in ("x", "y", "z", "a"):
- v = getattr(args, k, None)
- if v is not None:
- base_state[k] = float(v)
-
- # Load FK
- fk = RobotFK.from_file(args.robot)
-
- # Which markers belong to Hand/Palm/FingerA/FingerB?
- target_ids: set = set()
- for link_name in ("Hand", "Palm", "FingerA", "FingerB"):
- for m in fk.links.get(link_name, {}).get("markers", []):
- mid = int(m.get("id", -1))
- if mid >= 0:
- target_ids.add(mid)
-
- marker_size_m = float(
- fk.robot.get("vision_config", {}).get("MarkerSize", 0.025)
- )
-
- print(f"\n Target marker IDs: {sorted(target_ids)}")
- print(f" Marker size: {marker_size_m*1000:.1f} mm")
- print(f" Cameras: {len(args.det)}")
-
- # Collect world-frame poses from all cameras via solvePnP
- print("\n Per-camera solvePnP:")
- world_pose_obs = collect_marker_world_poses(
- args.det, args.pose, target_ids, marker_size_m, verbose=True
- )
-
- if not world_pose_obs:
- print("[ERROR] No target markers found in any detection file.")
- return 1
-
- # Fuse positions
- observed_m = fuse_marker_positions(world_pose_obs)
- print(f"\n Fused {len(observed_m)} marker world positions")
-
- # Optimise b, c, e
- result = optimize_bce(
- fk = fk,
- base_state = base_state,
- observed_m = observed_m,
- b_init = args.b_init,
- c_init = args.c_init,
- e_init = args.e_init,
- verbose = True,
- )
-
- if args.output and result["status"] == "ok":
- args.output.parent.mkdir(parents=True, exist_ok=True)
- with args.output.open("w", encoding="utf-8") as f:
- json.dump(result, f, indent=2)
- print(f"\n Saved → {args.output}")
-
- return 0 if result["status"] == "ok" else 1
-
-
-if __name__ == "__main__":
- raise SystemExit(main())
diff --git a/pipeline/4_v8_4b_revolute_angle.py b/pipeline/4b_revolute_angle.py
similarity index 100%
rename from pipeline/4_v8_4b_revolute_angle.py
rename to pipeline/4b_revolute_angle.py
diff --git a/pipeline/5_v8_camera_z_refine.py b/pipeline/5_camera_z_refine.py
similarity index 97%
rename from pipeline/5_v8_camera_z_refine.py
rename to pipeline/5_camera_z_refine.py
index 17bab20..1dfe6a7 100644
--- a/pipeline/5_v8_camera_z_refine.py
+++ b/pipeline/5_camera_z_refine.py
@@ -1,6 +1,6 @@
#!/usr/bin/env python3
"""
-5_v8_camera_z_refine.py
+5_camera_z_refine.py
-----------------------
Refines camera z-positions using the FK-predicted z of the Ellbow link.
@@ -10,7 +10,7 @@ Camera poses from step 2 are estimated from board markers alone. Board
markers are roughly coplanar (z ≈ 0), so the camera's z-height above the
board is poorly constrained and can be 20–40 % off.
-Once we have the elbow angle (step 3b, from 4_v8_4b_revolute_angle.py),
+Once we have the elbow angle (step 3b, from 4b_revolute_angle.py),
FK predicts where the Ellbow markers *should* be in world space. The
difference between FK-predicted z and triangulated z gives a global
z-offset for the cameras.
@@ -30,7 +30,7 @@ Algorithm
Usage
-----
-python 5_v8_camera_z_refine.py \\
+python 5_camera_z_refine.py \\
--angle path/to/v8_ellbow_angle.json \\
--robot path/to/robot.json \\
--aruco path/to/aruco_positions_optimized.json \\
@@ -96,7 +96,7 @@ def main() -> None:
description="5v8: refine camera z-positions using FK-predicted Ellbow marker z"
)
parser.add_argument("--angle", required=True,
- help="v8_ellbow_angle.json from 4_v8_4b_revolute_angle.py")
+ help="v8_ellbow_angle.json from 4b_revolute_angle.py")
parser.add_argument("--robot", required=True, help="robot.json")
parser.add_argument("--aruco", required=True,
help="aruco_positions_optimized.json (first-pass triangulation)")
diff --git a/run/robot_viewer.html b/run/robot_viewer.html
index 867c53f..8893e92 100644
--- a/run/robot_viewer.html
+++ b/run/robot_viewer.html
@@ -805,8 +805,8 @@ function rebuild() {
? Math.sqrt(opos.reduce((s,v,i) => s+(v-mpos[i])**2, 0))
: null;
const col = errMm == null ? 0x888888
- : errMm < 5 ? 0x3ecf6b
- : errMm < 15 ? 0xf59e0b
+ : errMm < 2 ? 0x3ecf6b
+ : errMm < 5 ? 0xf59e0b
: 0xff4f4f;
gObserved.add(makeSphere(op, 0.006, col));
@@ -852,7 +852,7 @@ function updateStats(errArr, nObs, nModel) {
const p90 = sorted[Math.floor(sorted.length*0.9)];
const max = sorted[sorted.length-1];
- const cls = v => v < 5 ? 'stat-ok' : v < 15 ? 'stat-warn' : 'stat-err';
+ const cls = v => v < 2 ? 'stat-ok' : v < 5 ? 'stat-warn' : 'stat-err';
el.innerHTML = `
Observed:${nObs}
@@ -863,7 +863,7 @@ function updateStats(errArr, nObs, nModel) {
p90:${p90.toFixed(1)} mm
Max:${max.toFixed(1)} mm
- 🟢 <5mm 🟡 5–15mm 🔴 >15mm
`;
+ 🟢 <2mm 🟡 2–5mm 🔴 >5mm`;
}
function setStatus(msg) {
diff --git a/run/run_pipeline_v8.bat b/run/run_pipeline_v8.bat
index cbda00c..ef2cbc2 100644
--- a/run/run_pipeline_v8.bat
+++ b/run/run_pipeline_v8.bat
@@ -90,7 +90,7 @@ REM ── STEP 3b: Elbow angle (robust to z-error) ─────────
echo.
echo [STEP 3b-v8] Estimate elbow angle
-python3 "..\pipeline\4_v8_4b_revolute_angle.py" ^
+python3 "..\pipeline\4b_revolute_angle.py" ^
--robot "%ROBOT_JSON%" ^
--aruco "%OUT_DIR%\aruco_positions_optimized.json" ^
--link Ellbow ^
@@ -109,7 +109,7 @@ for %%F in ("%OUT_DIR%\*_camera_pose.json") do (
set POSE_ARGS_V8=!POSE_ARGS_V8! -pose "%%F"
)
-python3 "..\pipeline\5_v8_camera_z_refine.py" ^
+python3 "..\pipeline\5_camera_z_refine.py" ^
--angle "%OUT_DIR%\v8_ellbow_angle.json" ^
--robot "%ROBOT_JSON%" ^
--aruco "%OUT_DIR%\aruco_positions_optimized.json" ^