Observable Point Cloud Filtering from Travel Path for Self-Localization in Wide-Area Environments

A filtering method for observable point clouds from travel path is proposed to improve the efficiency of each point in the point cloud map for self-localization of autonomous mobile robots. The method involves retaining only the point clouds along the planned travel path of the mobile robot that can be observed by it. These points constitute the observable point cloud map. By performing ray casting from the position of the depth sensor along the travel path, observable regions are extracted whereas unobserved point clouds, such as those behind obstacles or out of the sensor range, are removed. The effectiveness of the method is evaluated through comparative experiments involving self-localization using both an original wide-area point cloud map and the observable point cloud maps. A new metric called localization contribution per point is introduced to quantify the contribution of each point, in the point cloud map, to self-localization. The experimental results demonstrate the efficiency of the observable point cloud map when used for self-localization.