论文标题
具有匿名轴承测量的认证最佳相互定位
Certifiably Optimal Mutual Localization with Anonymous Bearing Measurements
论文作者
论文摘要
相互定位对于多机器人系统中的协调与合作至关重要。以前的工作通过假设测量值和接收到的进程估算之间的可用对应关系解决了这一问题，这些估计很难获得，尤其是对于统一的机器人团队而言。此外，大多数本地优化方法都要求进行初始猜测，并且对其质量敏感。在本文中，我们提供了一种仅使用匿名轴承测量值来制定一种新型混合构成四次约束二次问题（MIQCCP）的最佳最佳算法。然后，我们将原始的非凸问题放松到半芬矿编程（SDP）问题中，并获得使用现成的求解器使用证实的全局最佳选择。结果，我们的方法可以确定轴承置态对应关系，然后在特定条件下恢复机器人之间的初始相对姿势。我们将性能与在不同噪声水平下的广泛模拟上的局部优化方法进行比较，以显示我们在全球最优性和鲁棒性方面的优势。进行现实世界实验以显示实用性和鲁棒性。
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.
