Observation Model for Robust Localization of Mobile Robots based on Particle Filter in Changing Environments

For autonomous mobile robot navigation, localization is an essential capability. Given a equipped with 3D LiDAR sensor, environment map composed of point cloud built beforehand. The thus allowed to localize the position in using sensor scan data. However, sometimes changes due obstacles. Under changing environment, capability might be decreased. this challenge, we propose observation model framework particle filter based localization. In model, focus on distance and distribution clouds experiments, moved by operator virtual proposed able both original environments same accuracy. From results, finally show robustness for environments.