This paper addresses the challenges of point cloud data, which possess attributes such as large volume, high redundancy, and an unstructured nature. In light of time consumption and poor robustness issues arising when directly applying the Iterative Closest Point (ICP) algorithm to point cloud data with inadequate initial pose, an enhanced point cloud registration algorithm is proposed. First, the voxel grid filtering algorithm is used to simplify the point cloud; then extract the 3D Scale Invariant Feature Transform (3DSIFT) feature points of the point cloud, and combine with Fast Point Features Histograms (FPFH) to extract the features, next, use the direction vector threshold algorithm to remove the wrong matching point pairs, then, according to these features, the Random Sample Consensus (RANSAC) algorithm combine with the SVD algorithm is used to calculate the transformation parameters and complete the rough registration; Finally, the improved ICP algorithm based on KD-tree acceleration is used to complete the fine registration. The results show that the average registration accuracy of the proposed algorithm is 17.96%, 47.39%, 69.88% and 79.78% of the four comparison algorithms, and the registration time is shortened on the basis of weighing the registration accuracy.