The disadvantages of the nearest point iterative algorithm (ICP) are low registration efficiency in the big-data point cloud and strong dependence on the initial position of the registration point cloud. To overcome these disadvantages, this study proposes a method that combines the fast point cloud coarse registration method with the ICP algorithm. First, the original point cloud is sampled according to the voxel, and after extracting the key points with the normal vector feature, it is described by the fast point feature histogram (FPFH) algorithm. Subsequently, according to the vector angle feature of the key matching pair in the local neighborhood, the matching point pair is further simplified. Next, the reduced key sequence pair set is used to obtain the transformation parameter with the most interior points using the random sampling consensus algorithm (RANSAC), thereby completing the point cloud coarse registration. Finally, accurate registration is performed using the ICP algorithm on the basis of the point cloud coarse registration. Experimental results show that the registration efficiency and accuracy of the algorithm are improved for high-density point clouds.