Iterative closest point algorithm based on improved RANSAC

Abstract

Iterative Closest Point (ICP) algorithm is usually used for registration of three-dimensional model point clouds. It is a common and mature registration algorithm. The traditional ICP algorithm has certain mismatching and the point selection condition is relatively simple, we introduce an extra RANSAC mismatching removal step into the ICP algorithm. It takes extra consideration of the spatial geometry information of the point pair selection. It improves the accuracy of the algorithm while speeding up the convergence of registration. In addition, we discuss the influence of the number of points and the similarity threshold of the algorithm under the Stanford standard point cloud data set. Finally, Gaussian curvature is introduced to improve the accuracy of the algorithm and reduce the randomness and the time of the algorithm.