A Robust Kalman Filter Based Approach for Indoor Robot Positionning with Multi-Path Contaminated UWB Data

Ultra-Wide Band (UWB) is a widely used technology to provide real-time and accurate indoor localization to mobile robots, allowing their safe operation in the absence of a satellite-based navigation solution. However, UWB performance suffers from multi-path outliers when signals reflect on surfaces or encounter obstacles. This paper describes an approach to mitigate this issue, based on a M-Estimation Robust Kalman Filter (M-RKF) and leveraging an adaptive empirical variance model for UWB signals. The approach is validated experimentally on a ground robot.