# Calibration of Camera and Flash LiDAR System with a Triangular Pyramid Target
## 2 Related Works  
The calibration of the camera and LiDAR system contains intrinsic parameters and extrinsic parameters. The intrinsic calibration method of the camera varies with calibration objects. Traditional camera intrinsic calibration techniques such as [PERSON]'s algorithm [13] make use of a calibration board with a checkerboard pattern on it. The methods of [14] and [15] both focus on obtaining the coordinate of the feature points to solve the intrinsic parameters. The former designs a novel 3D calibration board with local gradient depth information and main plane square corner information(BWDC), while the latter uses a polygonal planar board. Most research on calibration between the camera and LiDAR ignored the camera calibration. The intrinsic parameters of LiDAR are completed by manufacturers. The method of [16] describes a way to obtain intrinsic parameters of 3D mechanical scanning LiDAR. The authors of [17] report on a calibration and stability analysis of the Velodyne VLP-16 LiDAR scanner. In this paper, we apply flash LiDAR calibrated in advance, and the method to obtain the intrinsic parameters of the camera is described amply in Section 3.2.1. Similar to camera intrinsic calibration, the extrinsic calibration method between the camera and LiDAR is closely related to the calibration object. There are currently two basic categories in calibration research. One is target calibration, and the other is targetless calibration. Early target calibration methods focus on camera and 1D or 2D LiDAR system. With the development of 3D LiDAR techniques, study concentration moved to the combination of camera and 3D mechanical scanning LiDAR. The indispensable geometric constraints can also be divided into two categories: vector constraint and feature point. The works of [18; 19; 20; 21; 22] utilize normal vectors of plane boards to find the expression in both image and point clouds, and then calculate the conversion between camera and LiDAR. A further paper [18] first adopts this idea to calibrate a 2D range finder and camera system. Another paper [19] further makes a toolbox online by extending this method to 3D LiDAR and camera system. The work of [20] uses three checkerboards and minimizes an energy function of correspondences between depth and image frames. Other works [21; 22] take several captures of calibration target and align normal vectors of these planes to iteratively obtain the calibration result. The work of [23] introduces the 3D line correspondence into the original plane correspondence and reduces the minimal number of poses required for this problem to one. The works of [14; 15; 24; 25; 26] apply the feature point correspondences calibration method. Two-dimensional feature points in images can be conveniently acquired, whereas obtaining the corresponding 3D feature points in point clouds is challenging as the point clouds of LiDAR are sparse. The authors of [14] apply 3D-2D point correspondences to calibrate the product of intrinsic matrix and extrinsic matrix, while [15] combines 3D-2D and 3D-2D point correspondences together. In recent years, a small number of studies focus on using the structural similarity between the LiDAR point cloud and image data to perform global matching and optimize the calibration results [27; 28; 29]. The method of [27] incorporates the laser reflectivity of 3D LiDAR points for calibration. Another paper [28] calibrates by maximizing the mutual information between the intensity of the LiDAR frame and the gray information of the image. A further paper [29] adopts the natural alignment of depth and intensity edges in combination with a Gaussian mixture model to accomplish the calibration. However, the targetless method need a large number of data and the sparse point clouds decrease the accuracy of the matching results. To improve the calibration accuracy and simplify manual operation, we apply flash LiDAR and propose a novel method to calibrate the intrinsic parameters of the camera and extrinsic parameters of the system.