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This paper presents a method for generating a 3D point-cloud map using multilayer laser scanner mounted on two-wheeled vehicle. The vehicle identifies its own 3D pose (position and attitude) in a laser-scan period using the normal-distributions transform (NDT) scan-matching method. The vehicle’s pose is updated in a period shorter than the laser-scan period using its attitude and angular velocity measured by an inertial measurement unit (IMU). The pose estimation is based on extended Kalman filter 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 laser-scan data, and a point-cloud map is generated based on the corrected laser-scan data. Experimental results of mapping a road environment using 32-layer laser scanner mounted on a bicycle show the performance of the proposed method in comparison with conventional methods of distortion correction of laser-scan data.

3D point-cloud mapping using laser scanner mounted on two-wheeled vehicle based on NDT scan matching