Improved Iterative Nearest Point Point Cloud Alignment Method

Aiming at the problems of slow convergence, long alignment time, and matching error due to low overlap rate in the traditional iterative nearest point (ICP) point cloud alignment algorithms, an improved ICP alignment algorithm based on chunked feature point extraction as the core and chunked alignment point cloud overlap rate as the constraint is proposed. First, the average distance density of the point cloud is calculated, the point cloud is chunked within the set number threshold, and the scale invariant feature transform (SIFT) feature points are extracted in parallel from the chunked point cloud, and the fast point feature histogram (FPFH) is used for feature description; then, the sampling consistency initial alignment (SAC-IA) algorithm is used to realize the matching of the point cloud, and the overlapping region of the point cloud is extracted based on the 50% inter block matching rate; finally, the initial attitude is calculated based on the matched feature points, and the overlapping part is used to achieve accurate alignment of the two point clouds. The experimental results show that the point cloud with low overlap rate after segmentation and overlapping region extraction can greatly shorten the running time and improve the registration accuracy.