running Multiview
This commit is contained in:
chk
@@ -488,6 +488,77 @@ def project_points(
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return projected.reshape(-1, 2)
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def compute_soft_constraint_residuals(
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robot_state: Dict[str, float],
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robot_markers: Dict[int, Dict[str, Any]],
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link_transforms: Dict[str, np.ndarray],
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robot: Dict[str, Any],
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enabled_constraints: Dict[str, ConstraintResult]
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) -> List[float]:
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"""
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Compute residuals from soft constraints (kinematic consistency, rigid body distances).
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Returns a list of constraint residuals to append to the total residual vector.
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"""
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residuals = []
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weight_scale = 0.1 # Weight for soft constraints relative to reprojection errors
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# Constraint 1: Rigid body distances within each link
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if enabled_constraints['RigidBodyDistances'].enabled:
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for link_name in ['Arm1', 'Ellbow', 'Arm2']:
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link_markers = [m for m in robot_markers.values() if m['link_name'] == link_name]
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if len(link_markers) < 2:
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continue
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# Compute all pairwise distances in world coords
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for i in range(len(link_markers)):
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for j in range(i + 1, len(link_markers)):
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m_i = link_markers[i]
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m_j = link_markers[j]
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pos_i = compute_marker_world_position(m_i, link_transforms)
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pos_j = compute_marker_world_position(m_j, link_transforms)
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dist_world = np.linalg.norm(pos_i - pos_j)
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# Reference distance in local coords
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dist_local = np.linalg.norm(m_i['position_m'] - m_j['position_m'])
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# Residual: difference should be zero (rigid body)
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error = dist_world - dist_local
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residuals.append(error * weight_scale * 0.1) # Very soft weight
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# Constraint 2: Fixed X-distances between links (Arm1 <-> Ellbow)
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if enabled_constraints['InterLinkXDistances'].enabled:
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arm1_markers = [m for m in robot_markers.values() if m['link_name'] == 'Arm1']
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ellbow_markers = [m for m in robot_markers.values() if m['link_name'] == 'Ellbow']
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if len(arm1_markers) >= 1 and len(ellbow_markers) >= 1:
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# Get first marker from each link
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m_arm1 = arm1_markers[0]
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m_ellbow = ellbow_markers[0]
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pos_arm1 = compute_marker_world_position(m_arm1, link_transforms)
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pos_ellbow = compute_marker_world_position(m_ellbow, link_transforms)
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# X-distance in world should match reference (relative position)
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# Since both rotate around X-axis at different points, we check consistency
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x_diff_world = pos_ellbow[0] - pos_arm1[0]
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x_diff_ref = m_ellbow['position_m'][0] - m_arm1['position_m'][0]
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error = x_diff_world - x_diff_ref
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residuals.append(error * weight_scale)
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return residuals
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def compute_marker_world_position(marker: Dict[str, Any], link_transforms: Dict[str, np.ndarray]) -> np.ndarray:
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"""Compute the world position of a marker given current link transforms."""
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link_transform = link_transforms[marker['link_name']]
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local_pos = np.concatenate([marker['position_m'], [1.0]])
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world_pos = (link_transform @ local_pos)[:3]
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return world_pos
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# ------------------------------------------------------------------
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# Optimization
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# ------------------------------------------------------------------
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@@ -520,12 +591,15 @@ def residuals_for_parameters(
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robot_markers: Dict[int, Dict[str, Any]],
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robot: Dict[str, Any],
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scale: float,
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default_state: Dict[str, float]
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default_state: Dict[str, float],
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enabled_constraints: Dict[str, ConstraintResult]
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) -> np.ndarray:
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robot_state, camera_params = unpack_parameters(params, len(views))
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link_transforms = compute_link_transforms(robot, robot_state, scale)
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residuals = []
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# Reprojection residuals (primary observation)
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for obs in observations:
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marker = robot_markers[obs['marker_id']]
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world_corners = compute_marker_world_corners(marker, link_transforms)
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@@ -535,6 +609,7 @@ def residuals_for_parameters(
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weight = math.sqrt(obs['confidence'])
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residuals.extend((diffs * weight).reshape(-1))
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# Weak priors on robot state
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for key in STATE_KEYS:
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diff = robot_state[key] - default_state.get(key, 0.0)
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if key in ('x', 'y', 'z', 'e'):
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@@ -543,9 +618,16 @@ def residuals_for_parameters(
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w = 0.01
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residuals.append(diff * w)
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# Soft constraints (kinematic consistency, rigid body constraints)
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soft_constraint_residuals = compute_soft_constraint_residuals(
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robot_state, robot_markers, link_transforms, robot, enabled_constraints
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)
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residuals.extend(soft_constraint_residuals)
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return np.asarray(residuals, dtype=np.float64)
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def estimate_uncertainty(result: Any, n_params: int) -> np.ndarray:
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if result.jac is None:
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return np.full(n_params, float('nan'), dtype=np.float64)
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@@ -563,9 +645,97 @@ def estimate_uncertainty(result: Any, n_params: int) -> np.ndarray:
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return np.sqrt(np.diag(cov))
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# ------------------------------------------------------------------
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# Output assembly
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# ------------------------------------------------------------------
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def print_constraint_sanity_check(
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robot_state: Dict[str, float],
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robot_markers: Dict[int, Dict[str, Any]],
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link_transforms: Dict[str, np.ndarray],
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robot: Dict[str, Any],
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enabled_constraints: Dict[str, ConstraintResult]
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) -> None:
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"""
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Print sanity checks for all constraints to verify the optimization result.
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"""
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print("\n" + "=" * 70)
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print("CONSTRAINT SANITY CHECKS (after optimization)")
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print("=" * 70)
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# Check 1: Rigid body distances
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if enabled_constraints['RigidBodyDistances'].enabled:
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print("\n1. RIGID BODY DISTANCES")
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for link_name in ['Arm1', 'Ellbow', 'Arm2']:
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link_markers = [m for m in robot_markers.values() if m['link_name'] == link_name]
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if len(link_markers) < 2:
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continue
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max_error = 0.0
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for i in range(len(link_markers)):
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for j in range(i + 1, len(link_markers)):
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m_i = link_markers[i]
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m_j = link_markers[j]
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pos_i = compute_marker_world_position(m_i, link_transforms)
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pos_j = compute_marker_world_position(m_j, link_transforms)
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dist_world = np.linalg.norm(pos_i - pos_j)
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dist_local = np.linalg.norm(m_i['position_m'] - m_j['position_m'])
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error = abs(dist_world - dist_local)
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max_error = max(max_error, error)
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status = "✓" if max_error < 1.0 else "⚠" if max_error < 5.0 else "✗"
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print(f" {link_name:10s}: max_error = {max_error:.3f} mm {status}")
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# Check 2: Inter-link X distances
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if enabled_constraints['InterLinkXDistances'].enabled:
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print("\n2. INTER-LINK X-DISTANCES")
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arm1_markers = [m for m in robot_markers.values() if m['link_name'] == 'Arm1']
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ellbow_markers = [m for m in robot_markers.values() if m['link_name'] == 'Ellbow']
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if len(arm1_markers) >= 1 and len(ellbow_markers) >= 1:
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m_arm1 = arm1_markers[0]
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m_ellbow = ellbow_markers[0]
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pos_arm1 = compute_marker_world_position(m_arm1, link_transforms)
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pos_ellbow = compute_marker_world_position(m_ellbow, link_transforms)
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x_diff_world = pos_ellbow[0] - pos_arm1[0]
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x_diff_ref = m_ellbow['position_m'][0] - m_arm1['position_m'][0]
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error = abs(x_diff_world - x_diff_ref)
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status = "✓" if error < 1.0 else "⚠" if error < 5.0 else "✗"
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print(f" Arm1 <-> Ellbow: error = {error:.3f} mm {status}")
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# Check 3: Arm2 sin(a) dependency
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if enabled_constraints['Arm2SinADependency'].enabled:
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print("\n3. ARM2 sin(a) DEPENDENCY (sanity check)")
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arm2_markers = [m for m in robot_markers.values() if m['link_name'] == 'Arm2']
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if len(arm2_markers) >= 2:
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# Check that markers with different Z values have different X spreads
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a_rad = math.radians(robot_state['a'])
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sin_a = math.sin(a_rad)
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cos_a = math.cos(a_rad)
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z_variations = {}
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for m in arm2_markers:
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z_local = m['position_m'][2]
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x_local = m['position_m'][0]
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pos_world = compute_marker_world_position(m, link_transforms)
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x_world = pos_world[0]
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# Expected: x_world = 90 + x_local * cos(a) - z_local * sin(a)
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x_expected = 90 * (robot.get('renderingInfo', {}).get('metric', 'mm') == 'mm' and 0.09 or 0.09) + x_local * cos_a - z_local * sin_a
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x_error = abs(x_world - x_expected)
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if z_local not in z_variations:
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z_variations[z_local] = []
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z_variations[z_local].append(x_error)
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max_error = max(max(errors) for errors in z_variations.values()) if z_variations else 0.0
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status = "✓" if max_error < 5.0 else "⚠" if max_error < 10.0 else "⚠"
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print(f" X-consistency with sin(a): max_error = {max_error:.3f} mm {status}")
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print(f" (Note: this is a consistency check, not a hard constraint)")
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print("=" * 70)
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def camera_position_world(rvec: np.ndarray, tvec: np.ndarray) -> np.ndarray:
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R, _ = cv2.Rodrigues(rvec)
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@@ -705,6 +875,16 @@ def main() -> None:
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}
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robot_markers = extract_markers(robot, scale)
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# Validate constraints
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print("\n" + "=" * 70)
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print("CONSTRAINT VALIDATION")
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print("=" * 70)
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enabled_constraints = validate_constraints(robot, robot_markers)
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for constraint_name, result in enabled_constraints.items():
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print(result)
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print("=" * 70)
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views, observations = collect_views_and_observations(args.detections, robot_markers)
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camera_guesses = []
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@@ -725,7 +905,7 @@ def main() -> None:
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progress['iter'] += 1
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now = time.time()
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if progress['iter'] == 1 or now - progress['last_print'] >= 1.0:
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res = residuals_for_parameters(xk, views, observations, robot_markers, robot, scale, default_state)
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res = residuals_for_parameters(xk, views, observations, robot_markers, robot, scale, default_state, enabled_constraints)
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cost = 0.5 * float(np.dot(res, res))
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delta_cost = None
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convergence = ''
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@@ -751,7 +931,7 @@ def main() -> None:
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result = least_squares(
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residuals_for_parameters,
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x0,
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args=(views, observations, robot_markers, robot, scale, default_state),
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args=(views, observations, robot_markers, robot, scale, default_state, enabled_constraints),
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jac='2-point',
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method='trf',
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loss='soft_l1',
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@@ -762,6 +942,10 @@ def main() -> None:
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robot_state, camera_params = unpack_parameters(result.x, len(views))
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uncertainties = estimate_uncertainty(result, len(result.x))
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# Print constraint sanity checks
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link_transforms = compute_link_transforms(robot, robot_state, scale)
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print_constraint_sanity_check(robot_state, robot_markers, link_transforms, robot, enabled_constraints)
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output = build_output(robot_state, uncertainties[:len(STATE_KEYS)], views, camera_params, observations, robot_markers, scale, robot, robot_json_path)
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Binary file not shown.
File diff suppressed because it is too large
Load Diff
@@ -1,31 +1,31 @@
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{
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"final_cost": 25086.49759096419,
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"final_cost": 24986.21700952237,
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"status": 0,
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"message": "The maximum number of function evaluations is exceeded.",
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"robot_state": {
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"state": {
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"x": 100.74147204793623,
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"y": 29.650679871841962,
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"z": -35.519338413527834,
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"a": -119.76253895286006,
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"b": 22.000000000000124,
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"c": 91.00000000000006,
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"e": 9.999999999999991
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"x": 100.65052085564044,
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"y": 29.661249073864987,
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"z": -35.948789524024555,
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"a": -119.85307495241203,
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"b": 21.99999999999959,
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"c": 90.99999999999991,
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"e": 10.000000000000025
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},
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"uncertainty": {
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"x_mm": 5958.291819057341,
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"y_mm": 1161.4246555312698,
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"z_mm": 548.5614212936815,
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"a_deg": 3375.9445110397996,
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"b_deg": 13762.367585907954,
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"c_deg": 13762.368814943879,
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"e_mm": 137623.68210441121
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"x_mm": 1490.1455410993876,
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"y_mm": 429.51650769405427,
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"z_mm": 477.27281993275335,
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"a_deg": 3533.703361743133,
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"b_deg": 13152.589314453906,
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"c_deg": 13152.589314528996,
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"e_mm": 131525.9153573403
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},
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"confidence": {
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"x": 1.7166198945166287e-259,
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"y": 3.630513831255408e-51,
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"z": 1.5006526335068334e-24,
|
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"a": 2.424335742250214e-147,
|
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"x": 1.922212631331247e-65,
|
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"y": 2.219908541429891e-19,
|
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"z": 1.8719954726562647e-21,
|
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"a": 3.4136023686230135e-154,
|
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"b": 0.0,
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"c": 0.0,
|
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"e": 0.36787944117144233
|
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@@ -1,6 +1,6 @@
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{
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"schema_version": "1.0",
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"created_utc": "2026-05-28T15:11:26Z",
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"created_utc": "2026-05-28T15:43:43Z",
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"vision_config": {
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"MarkerType": "DICT_4X4_250",
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"MarkerSize": 0.025
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@@ -46,7 +46,7 @@
|
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},
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"detections": [
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{
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"observation_id": "a0864373-2016-4c0c-be4d-c8b66b185bd7",
|
||||
"observation_id": "713817fc-5f5b-486d-850a-2b1915ab7eac",
|
||||
"type": "aruco",
|
||||
"marker_id": 102,
|
||||
"marker_size_m": 0.025,
|
||||
@@ -100,7 +100,7 @@
|
||||
"confidence": 0.9494437061622057
|
||||
},
|
||||
{
|
||||
"observation_id": "2d237a4b-33db-4a97-aad0-2b69922e5c27",
|
||||
"observation_id": "cc667670-1b39-4632-8f1c-1a5fdf0e0c7e",
|
||||
"type": "aruco",
|
||||
"marker_id": 243,
|
||||
"marker_size_m": 0.025,
|
||||
@@ -154,7 +154,7 @@
|
||||
"confidence": 0.8899728634608616
|
||||
},
|
||||
{
|
||||
"observation_id": "a01227d1-d980-435e-ad0f-453789a6e941",
|
||||
"observation_id": "0cd34a97-e5d7-4bbf-aac9-a4e203b31583",
|
||||
"type": "aruco",
|
||||
"marker_id": 210,
|
||||
"marker_size_m": 0.025,
|
||||
@@ -208,7 +208,7 @@
|
||||
"confidence": 0.5673043275049208
|
||||
},
|
||||
{
|
||||
"observation_id": "8044b2a7-1af6-457d-b112-8566c669e961",
|
||||
"observation_id": "5632f4b1-0db1-4a98-b8d3-9ebbbb4b126f",
|
||||
"type": "aruco",
|
||||
"marker_id": 247,
|
||||
"marker_size_m": 0.025,
|
||||
@@ -262,7 +262,7 @@
|
||||
"confidence": 0.8796777163094818
|
||||
},
|
||||
{
|
||||
"observation_id": "dac561f0-1939-438d-9ed1-5d057d5132dd",
|
||||
"observation_id": "75ae283a-9ba7-43bc-bb54-ba44e24e235f",
|
||||
"type": "aruco",
|
||||
"marker_id": 246,
|
||||
"marker_size_m": 0.025,
|
||||
@@ -316,7 +316,7 @@
|
||||
"confidence": 0.8015179238063419
|
||||
},
|
||||
{
|
||||
"observation_id": "33c3c11c-f7d2-4038-ac2c-e4332bcf5b79",
|
||||
"observation_id": "7c280cd2-7b94-476a-be3e-7d89e6e35ed3",
|
||||
"type": "aruco",
|
||||
"marker_id": 101,
|
||||
"marker_size_m": 0.025,
|
||||
@@ -370,7 +370,7 @@
|
||||
"confidence": 0.3325004465415643
|
||||
},
|
||||
{
|
||||
"observation_id": "3ae8ec72-f806-457c-8c60-403428368c0a",
|
||||
"observation_id": "2e554970-1749-4924-ac98-3ac4debec51b",
|
||||
"type": "aruco",
|
||||
"marker_id": 215,
|
||||
"marker_size_m": 0.025,
|
||||
@@ -424,7 +424,7 @@
|
||||
"confidence": 0.7952557920267838
|
||||
},
|
||||
{
|
||||
"observation_id": "755f017d-cb90-461d-bd57-25768e592696",
|
||||
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Reference in New Issue
Block a user