部分的相互観測下でのロボット群の方位に基づく相対位置推定
Bearing-based Relative Localization for Robotic Swarm with Partially Mutual Observations
部分的にしか互いを観測できないロボット群に対し、方位とオドメトリを統合した半正定値計画で最適かつスケーラブルに相対姿勢を推定する手法を提案。
著者: Yingjian Wang, Xiangyong Wen, Yanjun Cao, Chao Xu, Fei Gao
分類: cs.RO
原文アブストラクト
Mutual localization provides a consensus of reference frame as an essential basis for cooperation in multirobot systems. Previous works have developed certifiable and robust solvers for relative transformation estimation between each pair of robots. However, recovering relative poses for robotic swarm with partially mutual observations is still an unexploited problem. In this paper, we present a complete algorithm for it with optimality, scalability and robustness. Firstly, we fuse all odometry and bearing measurements in a unified minimization problem among the Stiefel manifold. Furthermore, we relax the original non-convex problem into a semi-definite programming (SDP) problem with a strict tightness guarantee. Then, to hold the exactness in noised cases, we add a convex (linear) rank cost and apply a convex iteration algorithm. We compare our approach with local optimization methods on extensive simulations with different robot amounts under various noise levels to show our global optimality and scalability advantage. Finally, we conduct real-world experiments to show the practicality and robustness.
