抄録
This paper presents a method for generating a 3D point-cloud map using multilayer laser scanner mounted on twowheeled vehicle. The vehicle identifies its own 3D pose (position and attitude) in a laser-scan period using the normaldistributions transform (NDT) scan-matching method. The vehicle’s pose is updated in a period shorter than the laserscan period using its attitude and angular velocity measured by an inertia measurement unit (IMU). The pose estimation is based on 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 show the performance of the proposed method.
