基于点特征的车载 LiDAR 与 GNSS/IMU 联合标定方法研究

The accuracies of extrinsic parameter calibration among the light detection and ranging (LiDAR), global navigation satellite systems (GNSSs), and inertial measurement unit (IMU) are the main factors affecting multisensor fusion and high-definition maps. This study presents a LiDAR and GNSS/IMU calibration method for unmanned vehicles that extracts the point cloud of a calibration pattern in real time and uses point features to calibrate extrinsic parameters. First, the coordinate transformation between LiDAR, GNSS, IMU, and Universal Transverse Mercator Grid System (UTM) was analyzed. Second, assuming that the installation plane of the IMU is parallel to the ground, the initial values of pitch and roll were calculated based on the normal vector of the ground in front of the vehicle, and the initial value of yaw was calculated using the offset of the calibration pattern center. Third, based on the assumption that the vehicle moves straight on a plane, constant attitude motion was used to transform the problem of solving the rotation angle into an optimization problem by solving the translation parameters according to the constraint of the unchanged UTM coordinate of the calibration pattern. The feasibility of the scheme is verified by analyzing the error of the algorithm, sensor measurement, and center-point matching. An unmanned mining truck was used to collect LiDAR, GNSS, and IMU synchronization data to test the proposed method. The experimental results demonstrate that the proposed method requires only a flat area and calibration pattern to solve the degradation problem of 6-DOFs extrinsic parameters resulting from 3-DOFs motion, and the calibrated extrinsic parameters ensure that the splicing error of a point cloud map within 20 m is less than 20 cm.