关键词: 点云数据, 自动配准, 特征分类, 匹配点对, 法向量夹角, 迭代最近点（ICP）

Abstract: In this paper, based on judging the angle between neighboring normal points of point cloud, an automatic registration algorithm is proposed to solve the problems of low accuracy and robustness of 3D point cloud auto-registration. The algorithm firstly computes the cosine of the angle between the normal vector of each point in the point cloud and the normal vector set of the neighboring point set, and then takes the cosine of each point in the neighborhood as the attribute feature vector of the point. The algorithm extracts feature points by feature classification. In accordance with the similarity of the geometric features, the matching pairs are preliminarily searched, then the Euclidean distance constraint is used to eliminate matching pairs. The least squares method is used to calculate the initial registration parameters, then the refined Iterative Closest Point（ICP） algorithm is used for fine matching. Experiments show that the algorithm has improved both in convergence speed and matching accuracy with respect to the classic ICP algorithm.

Key words: point cloud data, automatic registration, feature classification, matching pairs, normal vector angle, Iterative Closest Point（ICP）