appRobotBodyTrack

3D-Body-Tracking für Roboter aus Mehrkamera-ArUco-Bildern.

Input

Bilder: render_*.png
Intrinsics: render_*.npz
Konfiguration: robot.json

Output

Gelenke R⁷ → {x, y, z, a, b, c, e} (mm / Grad)

Interfaces

Eine Logik, drei Zugänge:

Python
CLI
REST (FastAPI)

Quickstart

Python

from scripts import estimate_from_dir

result = estimate_from_dir("data/Scene8", robot_json="robot.json")

print(result.joints)
print(result.confidence)

CLI

pip install -e .

python -m scripts data/Scene8 --robot robot.json
python -m scripts data/Scene8 --robot robot.json --cameras a,b,d

REST API

docker compose up

Request:

import requests

resp = requests.post(
    "http://localhost:8446/v1/estimate",
    files=[
        ("images", ("render_a.png", open("render_a.png", "rb"))),
        ("intrinsics", ("render_a.npz", open("render_a.npz", "rb"))),
    ],
)

print(resp.json()["joints"])

API

Endpoint	Methode	Zweck
`/v1/estimate`	POST	Bilder → Gelenke
`/v1/health`	GET	Status
`/v1/config`	GET	aktive Konfiguration

Response:

{
  "joints": {"x": 50.2, "y": -2.1, "z": 94.8, "a": 20.1},
  "confidence": {"x": "high", "b": "low"},
  "residual_rms": 1.45,
  "n_markers": 56,
  "processing_ms": 1240
}

Struktur

.
├── scripts/
├── config/robot.json
├── tests/
└── docker-compose.yaml

Deployment (Docker / Portainer)

Volume:

- /opt/approbot/config/robot.json:/config/robot.json:ro

Healthcheck:

curl http://<host>:8446/v1/health

Konfiguration

Zentrale Datei: robot.json

Verwendete Bereiche:

links
pose_estimation
vision_config
movements
units

Stack (minimal)

numpy
scipy
opencv (aruco)
fastapi + uvicorn

Naming

BodyTrack → Tracking (dynamisch) ✅
BodyMap → Modell / Repräsentation
BodySense → Wahrnehmung (low-level)

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