Sensor-based environmental perception is a crucial step for autonomous driving systems, for which an accurate calibration between multiple sensors plays a critical role. For the calibration of LiDAR and camera, the existing method is generally to calibrate the intrinsic of the camera first and then calibrate the extrinsic of the LiDAR and camera. If the camera's intrinsic is not calibrated correctly in the first stage, it isn't easy to calibrate the LiDAR-camera extrinsic accurately. Due to the complex internal structure of the camera and the lack of an effective quantitative evaluation method for the camera's intrinsic calibration, in the actual calibration, the accuracy of extrinsic parameter calibration is often reduced due to the tiny error of the camera's intrinsic parameters. To this end, we propose a novel target-based joint calibration method of the camera intrinsic and LiDAR-camera extrinsic parameters. Firstly, we design a novel calibration board pattern, adding four circular holes around the checkerboard for locating the LiDAR pose. Subsequently, a cost function defined under the reprojection constraints of the checkerboard and circular holes features is designed to solve the camera's intrinsic parameters, distortion factor, and LiDAR-camera extrinsic parameter. In the end, quantitative and qualitative experiments are conducted in actual and simulated environments, and the result shows the proposed method can achieve accuracy and robustness performance. The open-source code is available at https://github.com/OpenCalib/JointCalib.

翻译：基于传感器的环境感知是自动驾驶系统的关键环节，其中多传感器间的精确标定起着至关重要的作用。对于激光雷达与相机的标定，现有方法通常先标定相机内参，再标定激光雷达与相机的外参。若第一阶段相机内参标定不准确，则难以精确标定激光雷达-相机外参。由于相机内部结构复杂且缺乏有效的相机内参标定量化评估方法，实际标定中常因相机内参的微小误差导致外参标定精度下降。为此，我们提出一种新颖的基于靶标的相机内参与激光雷达-相机外参联合标定方法。首先，我们设计了一种新型标定板图案，在棋盘格周围添加四个圆形孔洞以定位激光雷达位姿。随后，基于棋盘格与圆形孔洞特征的重投影约束，构建代价函数以联合求解相机内参、畸变系数及激光雷达-相机外参。最后，在实际与仿真环境中开展定量与定性实验，结果表明所提方法在精度与鲁棒性方面均表现优异。开源代码请见 https://github.com/OpenCalib/JointCalib。