关键词: 三维重建, 点云配准, 组合特征点, 主成分分析

Abstract: Aiming at the problems of low accuracy， easy mismatching， and the descriptive error of single point features in the subsequent series of improved algorithms to point cloud shape， a point cloud registration algorithm based on point cloud combination feature point and principal component analysis is proposed. The intrinsic shape signatures is extracted from the point cloud， and the AC algorithm is used to extract the boundary points（BDRY） of the point cloud to form the combined feature points （ISS_BDRY）. The normal of the ISS_BDRY feature point is calculated and described by fast point feature histogram， and then the sampling consistency initial registration algorithm improved by principal component analysis SAC-IA is used to minimize the distance error between the main axes of the point cloud， thereby reducing the number of iterations in the point cloud fine registration process， and providing good pose for subsequent point cloud registration. In the fine registration stage， the iterative closest point registration algorithm introduced KD-Tree to accelerate search point cloud is used for registration. The experimental results show that compared with other single-point features， the registration accuracy of extracted combined feature points on Cat and Michael point clouds reaches 10-8 orders of magnitude， and the registration accuracy of the combined feature method is increased by 65.19% and 44.77%， respectively. Compared with ICP， NDT， Super 4PCS and other algorithms， the accuracy of the fine registration stage reaches 10-16 orders of magnitude， and it is almost completely coincide.

Key words: 3D reconstruction, point cloud registration, combined feature point, principal component analysis