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. 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. Firstly, we design a novel calibration board pattern, adding four circular holes around the checkerboard for locating the LiDAR pose. To this end, we propose a novel target-based joint calibration method of the camera intrinsic and LiDAR-camera extrinsic parameters. 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. If the camera's intrinsic is not calibrated correctly in the first stage, it isn't easy to calibrate the LiDAR-camera extrinsic accurately. 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. Sensor-based environmental perception is a crucial step for autonomous driving systems, for which an accurate calibration between multiple sensors plays a critical role.
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