Fault Tolerant Multi-Robot Cooperative Localization Based on Covariance Union
Fault Tolerant Multi-Robot Cooperative Localization Based on Covariance Union
复制标题
基于协方差并的容错多机器人协作定位
DOI：
10.1109/lra.2021.3100000
复制
发表时间：
2021-10-01
影响因子：
5.2
通讯作者：
Liu, Yaqiong
中科院分区：
文献类型：
--
作者：
Wang, Xuedong;Sun, Shudong;Liu, Yaqiong
本文研究了多机器人协作定位（CL）问题，这是一个具有挑战性的场景，其中机器人可能会收到虚假的传感器数据，可能导致状态估计不一致。针对这一问题，本文提出一种基于协方差并（CU）的完全去中心化的CL算法，简称DCL-CU。所提出的方法是容错的并且支持通用测量模型。进行了大量的蒙特卡洛模拟和一组真实实验来验证所提出的 DCL-CU 方法的性能。结果表明，DCL-CU 方法可以有效处理虚假传感器数据。
This paper studies the multi-robot cooperative localization (CL) problem, a challenging scenario in which robots may receive spurious sensor data, potentially causing inconsistent state estimates. To address this problem, this paper presents a fully decentralized CL algorithm based on covariance union (CU), referred to as DCL-CU. The proposed approach is fault-tolerant and supports generic measurement models. Extensive Monte Carlo simulations and a group of real-world experiments were conducted to verify the performance of the proposed DCL-CU approach. The results show that the DCL-CU approach can efficiently deal with spurious sensor data.