Dai Changshuai, Han Ting, Luo Yang, Wang Mengyi, Cai Guorong, Su Jinhe, Gong Zheng, Liu Niansheng
School of Computer Engineering, Jimei University, Xiamen 361021, China.
School of Geospatial Engineering and Science, Sun Yat-Sen University, Zhuhai 519082, China.
Sensors (Basel). 2024 Aug 13;24(16):5228. doi: 10.3390/s24165228.
With the advancement of computer vision and sensor technologies, many multi-camera systems are being developed for the control, planning, and other functionalities of unmanned systems or robots. The calibration of multi-camera systems determines the accuracy of their operation. However, calibration of multi-camera systems without overlapping parts is inaccurate. Furthermore, the potential of feature matching points and their spatial extent in calculating the extrinsic parameters of multi-camera systems has not yet been fully realized. To this end, we propose a multi-camera calibration algorithm to solve the problem of the high-precision calibration of multi-camera systems without overlapping parts. The calibration of multi-camera systems is simplified to the problem of solving the transformation relationship of extrinsic parameters using a map constructed by multiple cameras. Firstly, the calibration environment map is constructed by running the SLAM algorithm separately for each camera in the multi-camera system in closed-loop motion. Secondly, uniformly distributed matching points are selected among the similar feature points between the maps. Then, these matching points are used to solve the transformation relationship between the multi-camera external parameters. Finally, the reprojection error is minimized to optimize the extrinsic parameter transformation relationship. We conduct comprehensive experiments in multiple scenarios and provide results of the extrinsic parameters for multiple cameras. The results demonstrate that the proposed method accurately calibrates the extrinsic parameters for multiple cameras, even under conditions where the main camera and auxiliary cameras rotate 180°.
随着计算机视觉和传感器技术的发展，许多多相机系统正在被开发用于无人系统或机器人的控制、规划及其他功能。多相机系统的校准决定了其运行的准确性。然而，对没有重叠部分的多相机系统进行校准是不准确的。此外，在计算多相机系统的外部参数时，特征匹配点的潜力及其空间范围尚未得到充分认识。为此，我们提出一种多相机校准算法，以解决对没有重叠部分的多相机系统进行高精度校准的问题。多相机系统的校准被简化为使用由多个相机构建的地图来求解外部参数的变换关系的问题。首先，通过在闭环运动中对多相机系统中的每个相机分别运行SLAM算法来构建校准环境地图。其次，在地图之间的相似特征点中选择均匀分布的匹配点。然后，使用这些匹配点来求解多相机外部参数之间的变换关系。最后，将重投影误差最小化以优化外部参数变换关系。我们在多个场景中进行了全面实验，并提供了多个相机的外部参数结果。结果表明，所提出的方法能够准确校准多个相机的外部参数，即使在主相机和辅助相机旋转180°的情况下也是如此。
