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.
Topics & Concepts
Spurious relationshipCovarianceRobotFault toleranceComputer scienceMonte Carlo methodFault (geology)AlgorithmArtificial intelligenceDistributed computingMathematicsMachine learningStatisticsSeismologyGeologyIndoor and Outdoor Localization TechnologiesRobotics and Sensor-Based LocalizationTarget Tracking and Data Fusion in Sensor Networks