FPECMV: Learning-Based Fault-Tolerant Collaborative Localization Under Limited Connectivity

Abstract

Collaborative localization (CL) has garnered substantial attention in the field of robotics in recent years. Nonetheless, conventional CL algorithms have faced challenges when dealing with practical issues such as spurious sensor data and limited or discontinued observation and communication in real-world settings. This paper proposes a fault-tolerant practical estimated cross-covariance minimum variance update method (FPECMV) designed to tackle these challenges under limited connectivity. The proposed algorithm uses a CNN-based method to evaluate confidence, along with a fault isolation module to identify faults and manage spurious data in real time. The proposed fault isolation module utilizes relative measurement information that randomly occurs, without requiring high observation and communication prerequisites. Notably, the algorithm takes into account correlations among agents to maintain consistency in localization filters and attain accurate localization despite constraints posed by limited connectivity. To evaluate the performance of the proposed algorithm, experiments were conducted in a collaborative multi-robot environment with spurious sensor data and limited connectivity, using both the BULLET simulation and physical mobile robots. The experimental results indicate that the overall localization performance of the proposed algorithm is improved by 21.0% compared to the state of the art. The experiment results demonstrate the effectiveness of our algorithm in localizing group agents in challenging and intricate scenarios with limited connectivity and spurious sensor data.