匿名ベアリング測定による証明可能に最適な相互位置推定
Certifiably Optimal Mutual Localization with Anonymous Bearing Measurements
匿名のベアリング測定のみから混合整数二次制約二次計画問題を定式化し、半正定値計画緩和で大域最適解を証明可能に求める相互位置推定手法を提案した。
著者: Yingjian Wang, Xiangyong Wen, Longji Yin, Chao Xu, Yanjun Cao, Fei Gao
分類: cs.RO
原文アブストラクト
Mutual localization is essential for coordination and cooperation in multi-robot systems. Previous works have tackled this problem by assuming available correspondences between measurements and received odometry estimations, which are difficult to acquire, especially for unified robot teams. Furthermore, most local optimization methods ask for initial guesses and are sensitive to their quality. In this paper, we present a certifiably optimal algorithm that uses only anonymous bearing measurements to formulate a novel mixed-integer quadratically constrained quadratic problem (MIQCQP). Then, we relax the original nonconvex problem into a semidefinite programming (SDP) problem and obtain a certifiably global optimum using with off-the-shelf solvers. As a result, our method can determine bearing-pose correspondences and furthermore recover the initial relative poses between robots under a certain condition. We compare the performance with local optimization methods on extensive simulations under different noise levels to show our advantage in global optimality and robustness. Real-world experiments are conducted to show the practicality and robustness.
