一种新的结合三维点云骨架点和特征点的分割方法  

New Segmentation Method Combining Three-Dimensional Point Cloud Skeleton Points and Feature Points

作　　者：李仁忠[1] 刘哲闻 Li Renzhong;Liu Zhewen(School of Electronic and Information,Xi'an Polytechnic University,Xi'an,Shaanxi 710048,China)

机构地区：[1]西安工程大学电子信息学院,陕西西安710048

出　　处：《激光与光电子学进展》2020年第12期288-295,共8页Laser & Optoelectronics Progress

基　　金：中国纺织工业联合会科技指导性项目(2017071);陕西省高校科协青年人才托举计划项目(20180115)。

摘　　要：针对三维点云数据分割算法准确度低的问题,提出了一种结合点云骨架点和外部特征点的分割算法,所提算法可将传统方法分割不出来的局部小范围凸面体进行有效分割,从而使得三维点云数据分割得更为完善,为三维点云分割提供了新思路。利用C++及其开源的点云库进行编程,利用L1-中值算法对三维点云进行骨架点的提取,利用尺度不变特征变换算法进行特征点的提取,结合骨架点和特征点构建分割平面进行分割,再对剩余的特征点进行检测,再次构建分割平面进行分割,得到最终的结果。实验结果表明,该算法能对三维点云表面的小范围凸面体进行有效分割,提高了分割的准确性。Aiming at the problem of low accuracy of the segmentation algorithm for three-dimensional(3D)point cloud data,a new segmentation algorithm combining point cloud skeleton points and external feature points is proposed.This method can effectively segment local small-scale convex objects,which cannot be segmented by traditional methods.This would make the segmentation of 3D point cloud data more perfect and provide a new idea for the segmentation of 3D point clouds.In this paper,C++ and its open source point cloud library are used to program.First,L1 median algorithm is used to extract skeleton points from 3D point clouds.At the same time,feature points are extracted by scale-invariant feature transform algorithm.Then,a segmentation plane is constructed based on skeleton points and feature points,segmentation is conducted,and the remaining feature points are detected.At last,a segmentation plane is constructed again for segmentation,therefore getting the final result.Experimental results show that the algorithm can efficiently segment small-scale convex surface of 3D point clouds and improve the accuracy of segmentation.

关 键 词：成像系统 三维点云 骨架提取 特征点提取 点云分割 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]