An optimal pose estimator for map-based mobile robot dynamic localization: experimental comparison with the EKF

Theoretical solutions based on the matching of 2D range measurements with a map of the environment have been proposed to solve the robot localization problem. However most of them have not been experimented with in real conditions: the robot was stopped or it moved slowly during range data acquisition, and the environment was supposed to be static. We propose and evaluate a dynamic localization method based on feature matching. Experiments carried out in real cluttered indoor environments including people and unknown obstacles show the good performance of the proposed algorithm against the classical solution based on Kalman filtering.