Robust Decentralized Cooperative Localization for Multirobot System Against Measurement Outliers

Abstract

Decentralized cooperative localization (DCL) exhibits significant advantages in fault tolerance, practicality, and scalability, which serves as a crucial prerequisite for multi-robot system to achieve effective cooperative operations. Unfortunately, sensor measurements inevitably contain outliers due to the high dynamics of robots and uncertain environmental factors in practical applications. Most of the existing DCL algorithms concentrated on studying the cross-correlation between estimates, and performed poorly in the extreme cases with measurement outliers. To enhance the robustness and stability of multi-robot system, a robust DCL (RDCL) framework is proposed to suppress the impacts of unknown sensor outliers. We improve the measurement update process of the traditional DCL algorithm by employing two outlier-robust extend Kalman filter (EKF) methods to adaptively fuse outlier-contaminated measurements based on tracking the correlation between robots, which thereby achieves accurate and robust localization. The proposed RDCL framework is not restricted to a specific model and inhibitory effects on both absolute and relative measurements outliers. Simulation and experimental results demonstrate the potential and advantages of the proposed algorithm in terms of accuracy, robustness, and stability for multi-robot cooperative localization.