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：本文提出了一种针对单目相机与棱镜旋转固态激光雷达的外部标定方法。首先揭示了由花瓣形扫描模式导致的点云独特特性——空缺点（前景目标与背景物体之间的一种离群点）。不同于现有方法仅利用深度连续测量值，我们采用深度不连续测量值以保留更多有效特征并高效剔除空缺点。检测到的三维角点数量更大，因此比常规方法包含更丰富的先验信息，这些角点与重叠相机检测到的二维角点相结合，在提出的圆形度与矩形度约束下，可生成精确的外部参数估计。通过实际野外实验，采用定性与定量双重性能准则对算法进行评估，结果表明其性能优于现有算法。代码已开源在GitHub上。