Point-to-Plane metric point cloud registration algorithm with sampling consistency initial alignment (SAC-IA) is proposed to address the problems of the current point cloud alignment algorithm relying on initial poses, low convergence speed time and insufficient accuracy. Firstly, the raw point cloud data are uniformly downsampled using voxels to reduce the dataset size and computational overhead. Secondly, feature points are extracted and characterized using Fast Point Feature Histograms (FPFH) with a normal vector angle threshold. Meanwhile, the initial transformation matrix for the point cloud is then determined via SAC-IA. Finally, Point-to-Plane metric point cloud registration algorithm is used to complete the fine alignment based on the initial alignment. The experimental results show that the algorithm significantly improves the alignment accuracy and has higher alignment efficiency than the ICP algorithm based on KD-tree acceleration.