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.