Adaptive Multi-Robot Cooperative Localization Based on Distributed Consensus Learning of Unknown Process Noise Uncertainty

The unknown process noise covariance matrix (PNCM) problem inducing by poor calibration or time-varying environment has not been addressed in the 2-D multi-robot system. This problem will severely deteriorate the distributed cooperative localization consistency and accuracy, and is troublesome to solve due to small magnitude of the 2-D robot's PNCM. In this paper, the above issue is addressed by the following two steps. Firstly, the motion model of the 2-D robot is reconstructed to form a more estimable PNCM, from which a small-scale PNCM estimation algorithm is derived. Then the cooperative strategy consisting of a Kullback-Leibler average strategy and a recovery strategy is proposed to guarantee global PNCM estimation consensus and convergence, even if only partial robots access absolute measurement information. Theoretical consensus and convergence analyses are presented and comprehensive simulation and experimental tests are conducted to verify the effectiveness and superiority of the proposed algorithm.