Abstract: Abstract: In recent years, the pace of intelligent construction of coal mines in China has accelerated, and mine unmanned auxiliary transport vehicles have become an important content and acceptance standard of intelligent coal mines. The realization of unmanned driving of mining vehicles depends on accurate environmental perception, and the combination of lidar and camera can provide richer and more accurate environmental information. In order to ensure its effective fusion, external parameter calibration is required. In this paper, a non-target automatic calibration method for mine-used laser radar and camera is proposed. According to the characteristics of low-line-beam of mine-used vehicle-borne laser radar, a multi-frame point cloud fusion method is used to increase the point cloud density, and the effective target is extracted by panoramic segmentation. The initial external parameters are obtained by constructing the 2D-3D target centroid correspondence, and then the effective target point cloud is projected on the segmentation mask through the initial external parameters. The panoramic information matching function of the effective target image and the point cloud is constructed, and the external parameters are optimized by particle swarm optimization. The experimental results show that the method has a translation error of 0.055m and a rotation error of 0.394°, which reduces the translation error by 50.89% and the rotation error by 53.76% when the fused-frame point cloud is used as an input compared to a single-frame point cloud as an input: the translation error reduces by 43.88% and the rotation error reduces by 48.63% compared to the algorithm based on the semantic features, which indicates that this paper's method can effectively improve the automatic calibration accuracy of mining LiDAR and camera external parameters, which provides a research basis for the subsequent mining LiDAR data and image fusion.