Automatic point cloud registration algorithm based on the feature histogram of local surface.

ABSTRACT: In this paper, we present an efficient algorithm for point cloud registration in presence of low overlap rate and high noise. The proposed registration method mainly includes four parts: the loop voxel filtering, the curvature-based key point selection, the robust geometric descriptor, and the determining and optimization of correspondences based on key point spatial relationship. The loop voxel filtering filters point clouds to a specified resolution. We propose a key point selection algorithm which has a better anti-noise and fast ability. The feature descriptor of key points is highly exclusive which is based on the geometric relationship between the neighborhood points and the center of gravity of the neighborhood. The correspondences in the pair of two point clouds are determined acco

SUBMITTER: Lu J 

PROVIDER: S-EPMC7485869 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature