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Real-Time 6D Pose Estimation and Multi-Target Tracking for Low-Cost Multi-Robot System.

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This study introduces YolPnP-FT, a low-cost, real-time motion capture system for multi-robot cooperation. It enables accurate 6D pose estimation and tracking using a single depth camera, crucial for system validation.

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6D pose estimationRGB sensinglow-cost perceptionmulti-robot systemmulti-target trackingreal-time perception

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Reliable and affordable motion capture is essential for developing and validating multi-robot cooperation systems.
  • Traditional motion capture systems are often prohibitively expensive, limiting accessibility for research and development.

Purpose of the Study:

  • To develop a low-cost, real-time 6D pose estimation and tracking method for multi-robot systems.
  • To enable simultaneous robot classification, pose estimation, and multi-target tracking using a single depth camera.

Main Methods:

  • The proposed YolPnP-FT pipeline integrates YOLOv8 detection with a keypoint confidence filtering strategy (PnP-FT).
  • Gaussian-penalized Soft-NMS is employed to improve robustness against partial occlusions.
  • A combination of Mahalanobis and cosine distances ensures stable ID assignment for visually similar robots.

Main Results:

  • The system achieves an average position error below 0.009 m and an average angular error below 4.2° at camera heights under 2.5 m.
  • A stable tracking frame rate of 19.8 FPS is maintained at 1920 × 1080 resolution.
  • Perception outputs were validated in a CoppeliaSim simulation, confirming their utility for coordination tasks.

Conclusions:

  • The YolPnP-FT method offers a deployable, low-cost, and real-time perception solution for multi-robot systems.
  • This approach significantly reduces the cost barrier for motion capture in multi-robot research.
  • The system's accuracy and robustness make it suitable for real-world applications and downstream coordination tasks.