In response to the problems of weak applicability, low registration efficiency and poor robustness of existing point cloud registration algorithms when registering point clouds from different scenes, this paper proposes an efficient registration algorithm for models, indoor scene and outdoor scene point clouds. Firstly, voxel grid filtering is used to downsample the point cloud, and intrinsic shape signatures (ISS) is used to extract point cloud features. Then, fast point feature histograms (FPFH) are used to describe the feature points, and random sample consensus (RANSAC) algorithm is used for rough registration of point clouds. Finally, the voxelized generalized iterative closest point (VGICP) algorithm accelerated by a graphics processing unit (GPU) is used to achieve precise registration. Experimental results show that in the three-dimensional model, indoor and low overlap outdoor point clouds with noise, the proposed algorithm achieves high registration accuracy while only consuming 0.118 s, 0.306 s, and 0.648 s, respectively. Compared with existing registration algorithms, the registration efficiency is improved by 79.12%, 82.41%, and 88.28%, respectively. The proposed algorithm has high registration accuracy and efficiency in different application scenarios, and has stronger applicability and higher robustness.