Automatic Extrinsic Calibration of Dual LiDARs With Adaptive Surface Normal Estimation

Abstract

Solutions equipped with multiple light detection and ranging (LiDAR) systems have been widely used in several fields including mobile mapping, navigation, robot, and others. Accurate and robust extrinsic calibration between multiple scanners is necessary for the integration of point cloud data. An automatic method for the calibration of dual LiDARs with adaptive surface normal estimation is presented in this article. Specifically, this approach begins with environment detection from different positions and attitudes. A novel surface normal estimation method is conducted to take account of the uneven distribution of point cloud density and the edge information of planes. Finally, the calibration parameters are calculated by iteratively minimizing the cost function that consists of implicit point-to-plane distances. The experimental results on simulation and real-world data demonstrate that for different types of LiDAR, the proposed algorithm can achieve high-accuracy calibration in different scenes, without manual intervention. The rotation and translation calibration errors between Velodyne LiDARs are less than 1° and 0.02 m, respectively.