Research on motion distortion optimization algorithm of laser SLAM

Abstract

Aiming at the problems of inaccurate laser point cloud matching, large reference pose error and insufficient utilization of sensor observation data during 2D laser SLAM positioning and mapping, which lead to the motion distortion of lidar, a piecewise linear interpolation based method is proposed. Fusion of radar motion distortion optimization strategy and PL-ICP algorithm. The optimization algorithm first uses the piecewise linear interpolation method to fuse the data of the wheel odometer and the lidar, solve the robot pose corresponding to each laser point in the current frame of laser data, then convert all the laser points to the same coordinate system according to the solved pose, and finally the laser data scan matching is performed by the PL-ICP algorithm. In the experiment, the ICP and PL-ICP algorithms were compared for the radar data, and the SLAM algorithm equipped with the motion distortion optimization module was used for localization and mapping. The experimental results show that the motion distortion optimization algorithm effectively removes the motion distortion of low-precision lidar and improves the accuracy of mobile robot positioning and mapping.