论文标题
基于轴承的机器人群的相对定位，部分相互观察
Bearing-based Relative Localization for Robotic Swarm with Partially Mutual Observations
论文作者
论文摘要
相互定位提供了参考框架的共识，作为多机器人系统合作的基础。以前的工作已经开发了可认证且可靠的求解器，以用于每对机器人之间的相对转换估计。但是，与部分相互观察的机器人群相对恢复相对姿势仍然是一个无法解释的问题。在本文中，我们提供了一种完整的算法，具有最佳性，可扩展性和鲁棒性。首先，我们融合了统一的最小化问题中的所有进程和轴承测量。此外，我们将原始的非凸问题放松为半定义编程（SDP）问题，并具有严格的紧密保证。然后，为了在噪声情况下保持精确性，我们添加了凸（线性）等级成本并应用凸迭代算法。我们将方法与局部优化方法进行比较，在各种噪声水平下具有不同机器人量的广泛模拟，以显示我们的全球最优性和可伸缩性优势。最后，我们进行现实世界实验以显示实用性和鲁棒性。
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.
