Fault Tolerant Multi-Robot Cooperative Localization Based on Covariance Union

Xuedong Wang; Shudong Sun; Tiancheng Li; Yaqiong Liu

RA-L 202125 citations

Fault Tolerant Multi-Robot Cooperative Localization Based on Covariance Union

Xuedong Wang, Shudong Sun, Tiancheng Li, Yaqiong Liu

Abstract

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.

BibTeX

@inproceedings{ral2021_faulttolerantmul,
  title = {Fault Tolerant Multi-Robot Cooperative Localization Based on Covariance Union},
  author = {Xuedong Wang and Shudong Sun and Tiancheng Li and Yaqiong Liu},
  booktitle = {RA-L 2021},
  year = {2021}
}