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主驱动系统的关键步骤,对此,多个传感器之间的精确校准起着关键作用。对于LiDAR和相机的校准而言,现有方法通常是首先校准相机的内在,然后校准LIDAR和相机的外表。如果在第一阶段没有对相机的内在进行正确的校准,那么精确校准LIDAR-camera extrinsic 参数并非易事。由于相机内部结构复杂,而且对相机的内在校准缺乏有效的定量评价方法,在实际校准中,外部参数校准的精确度通常会因相机的内在参数的微小错误而降低。对于这一点,我们提出了一个新的基于目标的联合校准相机的内在和LIDAR-camera 外表参数。首先,我们设计了一个新型校准板模式,在检查板周围增加了四个圆孔,用于定位LIDAR 姿势。随后,根据相机的内在校准精确度校准度校准度校准度校准方法定义的成本函数,在RIRC-ROC的内校准校准校准度校准度校准校准校准中, 和圆的校准校准校准校准度环境中, 的校准校准和校准校准校准的校准的校准环境是最终校准的校准的校准的校准的校准的校准的校准和的校准的校准和的校准的校准和的校准和的校准的校准的校准的校准的校准。