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Event-Based Motion Capture System for Online Multi-Quadrotor Localization and Tracking.

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  • 1New Jersey Institute of Technology, 323 Dr Martin Luther King Jr Blvd, Newark, NJ 07102, USA.

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Summary
This summary is machine-generated.

This study introduces a low-cost motion capture system using event cameras for multi-quadrotor motion planning. The system, leveraging YOLOv5 and k-d trees, achieves high accuracy and scalability for drone tracking and control.

Keywords:
YOLOdatasets for robotic visionevent-based camerask-d treemotion capture systemsmotion coordinationmotion planningmulti-quadrotor systemsneural networkobject detectionpose estimation

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Area of Science:

  • Robotics and Control Systems
  • Computer Vision
  • Artificial Intelligence

Background:

  • Accurate motion capture is essential for multi-quadrotor system development.
  • Existing systems can be costly and complex.
  • Event cameras offer advantages in high-speed tracking scenarios.

Purpose of the Study:

  • To implement and validate a low-cost motion capture system for multi-quadrotor motion planning using an event camera.
  • To evaluate the performance of deep learning algorithms for multi-quadrotor detection on event-based data.
  • To assess the system's robustness to varying lighting conditions, camera motion, and scalability.

Main Methods:

  • Utilized an event camera for data acquisition.
  • Employed the You-Only-Look-Once (YOLOv5) deep learning network for real-time multi-quadrotor detection.
  • Implemented a k-dimensional (k-d) tree for object tracking.
  • Developed an optimization-based decentralized motion planning algorithm.
  • Conducted extensive experimental evaluations comparing deep learning algorithms and assessing system performance under various conditions.

Main Results:

  • YOLOv5 demonstrated a significant sampling/inference rate advantage (4.8× to 12×) over other detectors and a 1.14× advantage over YOLOv4.
  • YOLOv5 achieved superior precision (15%–18% better) and recall (27%–41% better) compared to state-of-the-art networks.
  • The system showed graceful performance degradation in low light and maintained high precision (94%) and recall (98%) despite severe camera motion.
  • Experiments with up to six quadrotors confirmed the system's scalability.
  • An open-source event camera dataset with over 10,000 annotated images was released.

Conclusions:

  • The developed low-cost motion capture system effectively supports multi-quadrotor motion planning.
  • YOLOv5 is a highly efficient and accurate deep learning model for multi-quadrotor detection using event camera data.
  • The system demonstrates robustness, scalability, and potential for real-world applications in drone coordination and control.