Fault tolerant cooperative localization using diagnosis based on Jensen Shannon divergence

Multi-robot systems have taken an important place in various applications, where at all times, the integrity of the location of robots must be ensured. This can be achieved by integrating a step of detecting and excluding sensor faults. In this article, a multi-sensor multi-vehicle Cooperative Positioning System (CPS) method tolerant to sensor faults is presented. The estimator used in this work is the informational form of the Kalman filter (KF), namely the Informational Filter (IF). To detect and isolate faults, the residuals generated are based on the divergence of Jensen Shannon
$(D_{JS})$
between the probability distributions predicted and corrected by the IF. These residuals, as they are a sum of two divergences of Kullback-Leibler include two tests: one compares the means and the other compares the covariance matrices. For the optimization of the threshold of the residuals, the operating characteristic of the receiver (frequently referred to as the ROC curve) is used. The approach is tested on real data acquired by three Turtlebot3 equipped with wheel encoders (for odometry), a gyroscope (for the yaw angle), a Marvelmind localization system (for the position), and an Optitrack system (for ground truth).