Registration of Point Clouds: A Survey

Abstract

The registration of point cloud is essentially to obtain a relatively accurate coordinate transformation matrix through operation, and unify the point cloud data from multiview into the particular coordinate system through rigid transformations such as rotation and translation. Generally speaking, the registration is to discover the position conversion matrix of the overlap between clouds, which have an important effect in the domain of robot and computer vision. The purpose of this article is to comprehensively summarize the current progress of point cloud registration from two dimensions: algorithm optimization methods and deep learning methods. This paper first points out the possible application fields and development direction of point cloud registration in the future, then makes a comparison between different algorithms, and finally makes a proper analysis of the advantages and disadvantages of each algorithm.