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所属单位：机电学院

发表刊物：ADVANCES IN MECHANICAL ENGINEERING

关键字：Point cloud match point feature histogram three-dimensional feature points double neighborhood

摘要：Flexible and robust point cloud matching is important for three-dimensional surface measurement. This article proposes a new matching method based on three-dimensional image feature points. First, an intrinsic shape signature algorithm is used to detect the key shape feature points, using a weighted three-dimensional occupational histogram of the data points within the angular space, which is a view-independent representation of the three-dimensional shape. Then, the point feature histogram is used to represent the underlying surface model properties at a point whose computation is based on the combination of certain geometrical relations between the point's nearest k-neighbors. The two-view point clouds are robustly matched using the proposed double neighborhood constraint of minimizing the sum of the Euclidean distances between the local neighbors of the point and feature point. The proposed optimization method is immune to noise, reduces the search range for matching points, and improves the correct feature point matching rate for a weak surface texture. The matching accuracy and stability of the proposed method are verified using experiments. This method can be used for a flat surface with weak features and in other applications. The method has a larger application range than the traditional methods.

ISSN号：1687-8140

是否译文：否

发表时间：2018-09-18

合写作者：廖文和,程筱胜,戴宁,Guo, Changye

通讯作者：崔海华