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FACT: Fast and Active Coordinate Initialization for Vision-based Drone Swarms (2403.13455v1)

Published 20 Mar 2024 in cs.RO

Abstract: Swarm robots have sparked remarkable developments across a range of fields. While it is necessary for various applications in swarm robots, a fast and robust coordinate initialization in vision-based drone swarms remains elusive. To this end, our paper proposes a complete system to recover a swarm's initial relative pose on platforms with size, weight, and power (SWaP) constraints. To overcome limited coverage of field-of-view (FoV), the drones rotate in place to obtain observations. To tackle the anonymous measurements, we formulate a non-convex rotation estimation problem and transform it into a semi-definite programming (SDP) problem, which can steadily obtain global optimal values. Then we utilize the Hungarian algorithm to recover relative translation and correspondences between observations and drone identities. To safely acquire complete observations, we actively search for positions and generate feasible trajectories to avoid collisions. To validate the practicability of our system, we conduct experiments on a vision-based drone swarm with only stereo cameras and inertial measurement units (IMUs) as sensors. The results demonstrate that the system can robustly get accurate relative poses in real time with limited onboard computation resources. The source code is released.

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References (28)
  1. Y. Gao, Y. Wang, X. Zhong, T. Yang, M. Wang, Z. Xu, Y. Wang, Y. Lin, C. Xu, and F. Gao, “Meeting-merging-mission: A multi-robot coordinate framework for large-scale communication-limited exploration,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 13700–13707, 2022.
  2. A. Marjovi, J. G. Nunes, L. Marques, and A. de Almeida, “Multi-robot exploration and fire searching,” in 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1929–1934, 2009.
  3. D. Saldaña, R. J. Alitappeh, L. C. A. Pimenta, R. Assunção, and M. F. M. Campos, “Dynamic perimeter surveillance with a team of robots,” in 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 5289–5294, 2016.
  4. X. Zhou, X. Wen, Z. Wang, Y. Gao, H. Li, Q. Wang, T. Yang, H. Lu, Y. Cao, C. Xu, et al., “Swarm of micro flying robots in the wild,” Science Robotics, vol. 7, no. 66, p. eabm5954, 2022.
  5. X. Zhou, J. Zhu, H. Zhou, C. Xu, and F. Gao, “Ego-swarm: A fully autonomous and decentralized quadrotor swarm system in cluttered environments,” in 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 4101–4107, 2021.
  6. F. Berlinger, M. Gauci, and R. Nagpal, “Implicit coordination for 3d underwater collective behaviors in a fish-inspired robot swarm,” Science Robotics, vol. 6, no. 50, p. eabd8668, 2021.
  7. R. D. Arnold, H. Yamaguchi, and T. Tanaka, “Search and rescue with autonomous flying robots through behavior-based cooperative intelligence,” Journal of International Humanitarian Action, vol. 3, no. 1, pp. 1–18, 2018.
  8. W. Hönig, J. A. Preiss, T. K. S. Kumar, G. S. Sukhatme, and N. Ayanian, “Trajectory planning for quadrotor swarms,” IEEE Transactions on Robotics, vol. 34, no. 4, pp. 856–869, 2018.
  9. Y. Wang, X. Wen, Y. Cao, C. Xu, and F. Gao, “Bearing-based relative localization for robotic swarm with partially mutual observations,” IEEE Robotics and Automation Letters, vol. 8, no. 4, pp. 2142–2149, 2023.
  10. Y. Wang, X. Wen, L. Yin, C. Xu, Y. Cao, and F. Gao, “Certifiably optimal mutual localization with anonymous bearing measurements,” IEEE Robotics and Automation Letters, vol. 7, no. 4, pp. 9374–9381, 2022.
  11. L. Riazuelo, J. Civera, and J. M. Montiel, “C2tam: A cloud framework for cooperative tracking and mapping,” Robotics and Autonomous Systems, vol. 62, no. 4, pp. 401–413, 2014.
  12. P.-Y. Lajoie, B. Ramtoula, Y. Chang, L. Carlone, and G. Beltrame, “Door-slam: Distributed, online, and outlier resilient slam for robotic teams,” IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 1656–1663, 2020.
  13. S. Hausler, S. Garg, M. Xu, M. Milford, and T. Fischer, “Patch-netvlad: Multi-scale fusion of locally-global descriptors for place recognition,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14141–14152, 2021.
  14. M. Li, G. Liang, H. Luo, H. Qian, and T. L. Lam, “Robot-to-robot relative pose estimation based on semidefinite relaxation optimization,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4491–4498, 2020.
  15. T. H. Nguyen and L. Xie, “Relative transformation estimation based on fusion of odometry and uwb ranging data,” IEEE Transactions on Robotics, vol. 39, no. 4, pp. 2861–2877, 2023.
  16. A. Martinelli, F. Pont, and R. Siegwart, “Multi-robot localization using relative observations,” in Proceedings of the 2005 IEEE International Conference on Robotics and Automation, pp. 2797–2802, 2005.
  17. C.-H. Chang, S.-C. Wang, and C.-C. Wang, “Vision-based cooperative simultaneous localization and tracking,” in 2011 IEEE International Conference on Robotics and Automation, pp. 5191–5197, 2011.
  18. A. Franchi, G. Oriolo, and P. Stegagno, “On the solvability of the mutual localization problem with anonymous position measures,” in 2010 IEEE International Conference on Robotics and Automation, pp. 3193–3199, 2010.
  19. A. Franchi, G. Oriolo, and P. Stegagno, “Mutual localization in a multi-robot system with anonymous relative position measures,” in 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3974–3980, 2009.
  20. A. Franchi, G. Oriolo, and P. Stegagno, “Mutual localization in multi-robot systems using anonymous relative measurements,” The International Journal of Robotics Research, vol. 32, pp. 1302 – 1322, 2013.
  21. J. Dong, E. Nelson, V. Indelman, N. Michael, and F. Dellaert, “Distributed real-time cooperative localization and mapping using an uncertainty-aware expectation maximization approach,” in 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 5807–5814, 2015.
  22. T. Nguyen, K. Mohta, C. J. Taylor, and V. Kumar, “Vision-based multi-mav localization with anonymous relative measurements using coupled probabilistic data association filter,” in 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 3349–3355, 2020.
  23. T. Qin, P. Li, and S. Shen, “Vins-mono: A robust and versatile monocular visual-inertial state estimator,” IEEE Transactions on Robotics, vol. 34, no. 4, pp. 1004–1020, 2018.
  24. H. Xu, Y. Zhang, B. Zhou, L. Wang, X. Yao, G. Meng, and S. Shen, “Omni-swarm: A decentralized omnidirectional visual–inertial–uwb state estimation system for aerial swarms,” IEEE Transactions on Robotics, vol. 38, no. 6, pp. 3374–3394, 2022.
  25. A. Carrio, S. Vemprala, A. Ripoll, S. Saripalli, and P. Campoy, “Drone detection using depth maps,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1034–1037, 2018.
  26. G. Jocher, A. Chaurasia, and J. Qiu, “Ultralytics YOLO.” https://github.com/ultralytics/ultralytics, Jan. 2023. Version 8.0.0.
  27. N. Aharon, R. Orfaig, and B.-Z. Bobrovsky, “Bot-sort: Robust associations multi-pedestrian tracking,” arXiv preprint arXiv:2206.14651, 2022.
  28. D. M. Rosen, L. Carlone, A. S. Bandeira, and J. J. Leonard, “Se-sync: A certifiably correct algorithm for synchronization over the special euclidean group,” The International Journal of Robotics Research, vol. 38, no. 2-3, pp. 95–125, 2019.

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