ヘルメットマウントLiDARによるNDT SLAMに基づく点群地図構築

抄録

This paper presents a 3D point-cloud mapping method in global navigation satellite systems (GNSS)-denied and dynamic environments using a light detection and ranging (LiDAR) mounted on a smart helmet that is worn by a rider of micro mobility. The distortion in the scan data from the LiDAR is corrected by estimating the helmet’s pose (3D positions and attitude angles) in a period shorter than the LiDAR scan period based on the information from normal distributions transform-based simultaneous localization and mapping (NDT SLAM) and an inertial measurement unit (IMU). The corrected scan data are mapped onto an elevation map, and the static and moving scan data, which originate from static and moving objects in the environments, respectively, are classified using the occupancy grid method. Only the static scan data are utilized to build a point-cloud map using NDT SLAM. The experimental results in a campus road environment qualitatively demonstrates show the effectiveness of the proposed method.