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Point-cloud Mapping using Lidar Mounted on Two-wheeled Vehicle based on NDT Scan Matching

This paper presents a method for generating a 3D point-cloud map using multilayer lidar mounted on twowheeled vehicle. The vehicle identifies its own 3D pose (position and attitude angle) in a lidar-scan period using the normal distributions transform (NDT) scan-matching method. The vehicle’s pose is updated in a period shorter than the lidar-scan period using its attitude angle and angular velocity measured by an inertial measurement unit (IMU). The pose estimation is based on the extended Kalman filter (EKF) under the assumption that the vehicle moves at nearly constant translational and angular velocities. The vehicle’s pose is further estimated in a period shorter than measurement period of the IMU using a linear interpolation method. The estimated poses of the vehicle are applied to distortion correction of lidar-scan data, and a pointcloud map is generated based on the corrected lidar-scan data. Experimental results of mapping a road environment using a 32-layer lidar mounted on a bicycle show the efficancy of the proposed method in comparison with conventional methods of distortion correction of lidar-scan data.