基于点云几何特征的错误匹配点对剔除算法

Elimination Algorithm of Mismatched Point Pairs Based on Point Cloud Geometric Feature

针对点云配准过程中存在错误匹配点对的问题,提出一种基于点云几何特征的双阈值剔除算法。依据点云几何特征在刚体变换过程中的平移旋转不变性,在初始匹配点集基础上,借助k近邻方法选取各查询点的近邻点并构成三点对,根据点间距离不变特性完成点对的初步筛选。在此基础上,采用曲面变分描述该三点对所在局部区域的几何特征,通过分析三点对的协方差矩阵,完成匹配点的最终筛选。实验结果表明,该方法可以有效剔除错误匹配点,且具有较高的配准精度。

Aiming at the problem of mismatched point pairs in the point cloud registration process,a double threshold elimination algorithm based on point cloud geometry feature is proposed.According to the geometric characteristics of the point cloud,the translational rotation invariance is obtained during the rigid body transformation process.On the basis of the initial matching point set,the nearest neighbor points of each query point are selected by the k-nearest neighbor method to form a three-point pair.According to the characteristics of the distance between points,the initial pairing is completed.On this basis,the geometrical features of the local region of the three-point pair are described by surface variation,and the final screening of the matching points is completed by analyzing the covariance matrix of the three-point pair.Experimental results show that the method can effectively eliminate the wrong matching points and has higher registration accuracy.