Abstract
This paper presents a Kalman-filter-based method of correcting distortion in 3D laser-scan data from in-vehicle multilayer laser scanner. A robot identifies its own 3D pose (position and attitude) in a laser-scan period using the normaldistributions transform (NDT) scan-matching method. Based on the pose information, the robot’s pose is predicted and smoothed in a period shorter than the scan period using Kalman filter under the assumption that the robot moves at nearly constant linear and turning velocities. The predicted and smoothed poses of the robot are applied to map laser-scan data onto a world coordinate frame. Subsequently, the robot again identifies its own 3D pose from the mapped scan data using NDT scan matching. This iterative process enables the robot to correct the distortion of laser-scan data and accurately map the laser-scan data onto the world coordinate frame. Experimental results of SLAM in a road environment show the performance of the proposed method.
