Resilient Decentralized Cooperative Localization for Multisource Multirobot System

Abstract

Although cooperative localization is fundamental to multi-robot systems, current algorithms suffer from the tracking of interdependencies, information fusion from multiple sources, and restriction to specific measurement models. To improve the accuracy of localization algorithms for multi-robot systems and reduce the impact of uncertainty in multi-source measurement information, this paper proposes a resilient decentralized cooperative localization algorithm. We modify the measurement update procedure of the traditional decentralized cooperative localization algorithm to track inter-robot correlations and ensure the independence of the measurement update procedure of the elemental filters. We use optimal information fusion algorithms to fuse multi-source information, and determine the overall estimate of every robot through a weighted sum of multi-source estimates, thereby achieving accurate localization. To enhance the robustness of the multi-robot localization system, an online validation module is added to validate the multi-source estimates. The proposed cooperative localization framework is decentralized and not restricted to specific models. Simulations results show that the proposed algorithm improves localization accuracy and resilience of the multi-robot system compared to existing cooperative localization algorithms. Experimental results using real-world dataset demonstrate that our proposed algorithm can achieve a localization accuracy with an average ARMSE of 0.68 m, and it is 34% better than that of the traditional DCL algorithm.