Point Cloud registration based on iterative closest point

Abstract

Point cloud registration is widely used in computer vision, robotics and other fields. It is a process of finding spatial transformations (such as scaling, rotation, and translation) that align two point clouds. The purpose of finding this transformation includes merging multiple data sets into a globally consistent model (or coordinate system) and mapping new metrics to known data sets to identify features or estimate their pose. The popular solution of point cloud registration is iterative closest point (ICP). Two identical 3D model can coinside if we move and rotate one of them to match the other. Point cloud registration algorithm based on ICP can be divided into two steps: rough registration and fine registration. Rough registration refers to point cloud matching when the transformation parameters between two point clouds are completely unknown. The main purpose is to provide initial transformation parameters for fine registration. The fine registration is to obtain a more accurate transformation with the given initial transformation. Through experiments, this paper verifies that ICP can obtain desirable point cloud registration results.