Fault tolerant multi-sensor fusion for multi-robot collaborative localization

Abstract

In the last decades, the multi-robot system has been widely investigated in mission that cannot be achieved by using a single robot or in area presenting danger to human life. Each robot needs to have an accurate position estimation of itself and of the others in the team. In this paper, we present a framework for localizing a group of robots with sensors Fault Detection and Exclusion (FDE) step. The Collaborative Localization (CL) is formulated using the Information Filter (IF) estimator which is the informational form of the Kalman Filter (KF). Residual tests calculated in term of divergence between the priori and posteriori distributions of the IF are developed in order to perform the FDE step. These residuals are based on the Kullback-Leibler Divergence (KLD) and they are generated from two tests: One acts on the means, and the other acts on the covariance matrices of the probability data distributions. Optimal thresholding method using entropy criterion is discussed and developed. Finally, the validation of this framework is studied on real experimental data from a group of robots.