Accurate and reliable sensor calibration is critical for fusing LiDAR and inertial measurements in autonomous driving. This paper proposes a novel three-stage extrinsic calibration method between LiDAR and GNSS/INS for autonomous driving. The first stage can quickly calibrate the extrinsic parameters between the sensors through point cloud surface features so that the extrinsic can be narrowed from a large initial error to a small error range in little time. The second stage can further calibrate the extrinsic parameters based on LiDAR-mapping space occupancy while removing motion distortion. In the final stage, the z-axis errors caused by the plane motion of the autonomous vehicle are corrected, and an accurate extrinsic parameter is finally obtained. Specifically, This method utilizes the planar features in the environment, making it possible to quickly carry out calibration. Experimental results on real-world data sets demonstrate the reliability and accuracy of our method. The codes are open-sourced on the Github website. The code link is https://github.com/OpenCalib/LiDAR2INS.

翻译：传感器的高精度与高可靠性标定是实现自动驾驶中激光雷达与惯性测量数据融合的关键。本文提出了一种面向自动驾驶场景的三阶段LiDAR与GNSS/INS外参标定方法。第一阶段通过点云表面特征快速标定传感器间的外参参数，能够在极短时间内将初始大范围误差收敛至较小误差区间；第二阶段基于LiDAR建图空间占据特性，在去除运动畸变的同时进一步优化外参参数；最终阶段针对自动驾驶汽车平面运动导致的z轴误差进行修正，从而获得精确的外参参数。本方法利用环境中的平面特征实现快速标定，在真实数据集上的实验结果表明了其可靠性与准确性。相关代码已在GitHub平台开源，代码链接为https://github.com/OpenCalib/LiDAR2INS。