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.
Note to Practitioners
—This work is motivated by the inaccurate PNCM problem existing in the 2-D homogeneous multi-robot system, whose PNCM is very small in magnitude. The pose accuracy of the 2-D mobile robot, which relies on the sensor precision, is generally not enough to estimate such small-scale PNCM. Most of the existing PNCM estimation algorithms are regarding to simple target tracking models whose PNCMs are relatively large in magnitude. Furthermore, a few small-scale PNCM estimation algorithms make crucial assumptions about the PNCM, which limits their practicality. This paper proposed a novel small-scale PNCM estimation algorithm and an efficient cooperative strategy to facilitate global PNCM estimation consensus and convergence, without making any assumptions about the PNCM. The consensus and convergence analyses are provided to further demonstrate the effectiveness of the proposed adaptive cooperative localization algorithm. The proposed algorithm has been evaluated via simulation, public dataset and physical experiment.