Robust State-Space Estimation for Mobile Robot Localization

Abstract

This paper deals with the estimation of the localization of mobile robots based on robust Kalman filter. The data provided by odometric and sonar sensors are fused together using a robust filter to provide robot position and orientation estimates at each sampling time. Simulation results are presented, with a comparative study between the robust Kalman filter and the standard extended Kalman filter.