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

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