This paper proposes a point cloud registration algorithm based on normal distribution transform (NDT) and feature point detection to address the shortcomings of traditional iterative closest point (ICP) algorithms, such as a large amount of calculation, low efficiency, and ease of being affected by the initial pose of the point cloud. The algorithm employs a “coarse and fine” registration strategy. First, the point cloud is preprocessed; thereafter, the NDT algorithm is used to coarsely register the processed point cloud for providing a more ideal initial pose for fine registration. Next, the 3D-Harris feature point detection algorithm is used to extract the point cloud feature points. Finally, the ICP algorithm is used to finely register the point cloud set after the feature point extraction to obtain an optimal solution. The simulation results show that when compared to the traditional algorithm, the algorithm used in this paper improves the efficiency and the accuracy of point cloud registration.