A Density-Assisted Particle Filter for Mobile Robot Localization with Uncertain Environment MAP

We present in this paper a modified density-assisted particle filter for indoor mobile robot localization in a situation where the environment map is subject to random uncertainties and is not perfectly known to the tracker. The proposed filter jointly estimates the robot´s pose and the environment map parameters combining raw measurements from a range-finding laser scanner and the robot´s odometric data. Experiments with real data show promising results even in adverse scenarios with abrupt maneuvers and heavily cluttered environments.