Adaptive Recursive Decentralized Cooperative Localization for Multirobot Systems With Time-Varying Measurement Accuracy

Decentralized cooperative localization (DCL) is a promising method to determine accurate multi-robot poses (i.e., positions and orientations) for robot teams operating in an environment without absolutenavigation information. Existing DCL methods often use fixed measurement noise covariance matricesfor multi-robot pose estimation, however, their performance degrades when the measurement noisecovariance matrices are time-varying. To address this problem, in this paper, a novel adaptive recursiveDCL method is proposed for multi-robot systems with time-varying measurement accuracy. Each robotestimates its pose and measurement noise covariance matrices simultaneously in a decentralized mannerbased on the constructed hierarchical Gaussian models using the variational Bayesian approach. Sim-ulation and experimental results show that the proposed method has improved cooperative localization accuracy and estimation consistency but slightly heavier computational load than the existing recursive DCL method.