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

Abstract

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