A principal component analysis (PCA)-based point cloud registration strategy is proposed by analyzing the algorithm registration process, and the PCA registration design is added to the iterative process of the iterative closest point (ICP) algorithm to solve the problem, wherein the ICP algorithm easily falls into a local minimum. In addition, the registration is time-consuming.First, the center of gravity method is used to make the center of gravity of the reference point cloud coincide with the point cloud to be registered before the first iteration to determine the initial pose. Second, the PCA is used to master the point cloud to be registered and the reference point cloud in each iteration of the ICP algorithm. After performing PCA, the first three principal component eigenvectors are selected, and corresponding matching through posture transformation are performed, so that after the initial registration of the two-point clouds is complete, the Euclidean distance is used to find the closest point to complete the subsequent registration process.The classic ICP algorithm with three initial pose determination methods, the mainstream algorithm of the literature, and the proposed iterative PCA algorithm with three initial pose determination methods are selected for comparative analysis in this study. The results show that while the first two algorithms are not able to register, the proposed algorithm not only avoids falling into the local minimum but also improvs in speed and accuracy. The number of iterations is 10 times, which takes 19.427939 s and the registration error is 2.1932, improving the overall registration performance.