Addressing the challenges faced by existing point cloud registration methods, which are often easily affected by noise, point cloud size, and initial pose, this study proposes a robust and efficient registration method to enhance accuracy. First, the original point cloud undergoes voxel gridding. Second, based on the signature of histograms of orientation (SHOT) feature descriptor, a strengthened SHOT keypoint set is extracted for rough matching using the four-point ensemble (4PCS) algorithm. This step achieves the initial registration of the source and target point clouds. Finally, a dual-scale, feature-constrained strategy is introduced to extend the generalized iterative closest point (ICP) to the voxel level, accurately registering two point clouds that have good initial poses. Herein, extensive experiments are conducted on the Stanford public point cloud dataset, as well as a real-world dataset, to evaluate the applicability of the algorithm across various scenarios. Experimental results show that the point cloud registration algorithm exhibits strong reliability and robustness, effectively handling different initial positions, overlap rates, and scales of point clouds. Moreover, the registration accuracy in real-world scenarios is improved by 37.6% and 23.2% compared to those corresponding to the ICP and fast four-point set (Super-4PCS) algorithms, respectively.