Aiming at the problems of low resolution and poor quality of point cloud data collected by the current mainstream low-beam LiDAR

and low calibration precision of the camera

a camera and LiDAR calibration method based on multi-position correlation points is proposed.Firstly

ArUco markers are used to recover the three-dimensional coordinates of the calibration board corner points in the image

and the key corner points of the LiDAR point cloud are extracted by fitting the edge point cloud of the calibration board.Then

multi-frame cumulative clustering is used to extract corresponding features from the image and the point cloud to obtain the external parameters of the position of this calibration board.Finally

the weights are set according to the reprojection error at each position

and the external parameters at each position are weighted and calculated to obtain the external parameters between the camera and the LiDAR.The experimental results show that the proposed calibration method achieves a translation error of less than 0.57cm

a rotation error of less than 0.19°

and a reprojection error of less than 0.008440

which effectively improves the calibration precision of the low-beam LiDAR and the camera.