激光与光电子学进展, 2019, 56 (9): 091403, 网络出版: 2019-07-05
一种基于高斯映射的三维点云特征线提取方法 下载: 1363次
Method for Extraction of Feature Lines of Three-Dimensional Laser Point Cloud Based on Gaussian Map
激光光学 特征提取 k近邻搜索 高斯映射 K-means聚类 轮廓系数 laser optics feature extraction k-nearest neighbor search Gaussian map K-means clustering silhouette coefficient
摘要
提出了一种基于高斯映射的K 均值方法,先对目标点进行k 近邻搜索,再对由目标点及其近邻点组成的三角形集合的单位法向量进行高斯映射。选用轮廓系数作为聚类有效性指标,确定出最佳聚类数,根据不同曲面聚类分布的规律,得到三维激光点云模型的特征线。对比实验结果表明,所提方法评价指标简单易用且噪声少,可以完整高效地提取出规则点云以及不规则点云的特征线。
Abstract
A K-means clustering method is proposed based on Gaussian map. First, we search the neighboring points of the target points using a k-nearest neighbor search. Gaussian map is then performed on the normal vectors of the set of triangles consisting of the target point and its neighbors. The silhouette coefficient is selected as the cluster validity index to determine the optimal cluster number. According to the clustering distribution of different surfaces, the feature lines of the three-dimensional laser point cloud model are obtained. The experimental results show that the proposed evaluation index is easy to use and has less noise than other indexes. It can extract the feature lines of regular and irregular point clouds completely and efficiently.
徐卫青, 陈西江, 章光, 袁俏俏. 一种基于高斯映射的三维点云特征线提取方法[J]. 激光与光电子学进展, 2019, 56(9): 091403. Weiqing Xu, Xijiang Chen, Guang Zhang, Qiaoqiao Yuan. Method for Extraction of Feature Lines of Three-Dimensional Laser Point Cloud Based on Gaussian Map[J]. Laser & Optoelectronics Progress, 2019, 56(9): 091403.