Sequential Sensor Fusion for Slip Estimation in Mobile Robots

Autonomous localization of Wheeled Mobile Robots (WMRs) in challenging extraterrestrial environments greatly depends on wheel slip estimation. In this paper, we propose a novel sequential track-to-track fusion for multi-sensor networks of unscented Kalman filters that has immediate application to wheel slip estimation of WMRs. Compared to current fusion techniques, the algorithm exhibits enhanced consistency through the propagation and utilization of cross-correlations, as well as improved computational efficiency via the implementation of a sequential scheme for fusing local estimates. As a case study, the slip estimation in a six-wheel planetary WMR with purely proprioceptive sensors is considered, where the steerable wheel sets form a network of sensors. We compare the novel slip estimator's performance with rival methods in a high-fidelity software-in-the-loop simulation and demonstrate its ability to achieve a balance between consistency, accuracy, and speed in real-time applications.