ICP registration based on 3D point clouds feature descriptor

Abstract

Widely used in 3D modeling, reverse engineering and other fields, point cloud registration aims to find the translation and rotation matrix between two point clouds obtained from different perspectives, and thus correctly match the two point clouds. As the most common point cloud registration method, ICP algorithm, however, requires a good initial value, not too large transformation between the two point clouds, and also not too much occlusion; otherwise, the iteration would fail to converge to a correct result. To solve this problem, this paper proposes an ICP matching algorithm based on the local features of point clouds. With this algorithm, a robust and efficient three-dimensional local feature descriptor is firstly designed by combining the density, curvature, and normal information of the point clouds, then based on the feature description, the correspondence between the point clouds and also the initial registration result are found, and finally, the aforementioned result is used as the initial value of ICP to achieve fine tuning of the registration result. The experimental results on public data sets show that the ICP algorithm boasts good registration precision and robustness, and a fast running speed as well.