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通过引入特征点和改进最近点迭代法,提出了一种在三维扫描系统中对三维点云数据进行配准的方法。该方法通过对特征点的提取,首先得到一组匹配点对,然后运用SVD矩阵分解算法求出转换参数R和T,进而以此作为最近点迭代法的初始值,并对最近点的求法和迭代截止条件作了改进,得到了很好的配准效果。该文论述了该方法的基本原理,并通过不同视觉下物体三维测量点云数据配准的应用实例证明了该方法的有效性。
By introducing feature points and improving nearest-neighbor iterative method, a method of registering 3D point cloud data in 3D scanning system is proposed. Firstly, a set of matching point pairs is obtained by extracting the feature points, and then the SVD matrix decomposition algorithm is used to find the conversion parameters R and T, which are used as the initial value of the nearest point iteration method. The method of the nearest point The iterative cut-off conditions have been improved, and a good registration result has been obtained. This paper discusses the basic principle of the method and proves the effectiveness of the method through the application examples of point cloud data registration of objects in different visual fields.