Map-Based Robust Localization For Indoor Mobile Robots

Standard Monte-Carlo Localization (MCL) cannot work well in dynamic environments and it usually requires a large number of particles to obtain stable and accurate pose information. To cope with these issues, this paper proposes an improved MCL approach inspired by motion detection and scan matching for robust localization of indoor mobile robots. Different from standard MCL, the proposed approach incorporates a motion detection module to handle dynamic environments, and it also considers the ray tracing characteristics of each laser beam to optimize the particle distribution. The proposed approach is implemented under Robot Operating System (ROS), and comparative experimental results are provided to illustrate the superior performance of the proposed approach.