This letter proposes an extrinsic calibration approach for a pair of monocular camera and prism-spinning solid-state LiDAR. The unique characteristics of the point cloud measured resulting from the flower-like scanning pattern is first disclosed as the vacant points, a type of outlier between foreground target and background objects. Unlike existing method using only depth continuous measurements, we use depth discontinuous measurements to retain more valid features and efficiently remove vacant points. The larger number of detected 3D corners thus contain more robust a priori information than usual which, together with the 2D corners detected by overlapping cameras and constrained by the proposed circularity and rectangularity rules, produce accurate extrinsic estimates. The algorithm is evaluated with real field experiments adopting both qualitative and quantitative performance criteria, and found to be superior to existing algorithms. The code is available on GitHub.

翻译：本信提议对一对单眼照相机和棱柱悬浮固态激光雷达采用外部校准方法。通过花型扫描模式测量的点云的独特特性首先作为空点披露,即前景目标与背景对象之间的一种外缘。与仅使用深度连续测量的现有方法不同,我们使用深度不连续测量方法来保留更有效的特征并有效清除空点。因此,被检测到的3D角中,比通常多的是比通常更可靠的先验信息,与2D角一起,由重叠的相机探测到,并受拟议的圆形和矩形规则限制,得出准确的外形估计。算法通过采用定性和定量性性能标准的实际实地实验进行评估,并被认为优于现有的算法。在GitHub上可以找到这一代码。