In order to solve the problems such as the low registration efficiency of the iterative nearest point algorithm and the accuracy depending on the good initial posture of the point cloud, this paper proposes a point cloud coarse registration method based on the internal shape descriptor and the improved fast point feature histogram. Firstly, voxel filtering is used to preprocess the initial point cloud, and then the internal shape description sub-algorithm is adopted to extract the feature points of the initial point cloud and find the normal vector corresponding to the feature point, and the fast point feature histogram is used to extract the feature vector of the feature point. According to the difference between the angle of the feature vector and the normal vector, the feature points are coarsely registered, which provides a good initial posture for the fine registration of the iteration of the nearest point registration algorithm. Experimental results show that the point cloud registration algorithm proposed in this paper can provide a better initial posture for the fine registration of the nearest point algorithm. Compared with several traditional registration methods, the proposed method overcomes the limit of the number of point clouds of registration, and further improves the efficiency and accuracy of registration.