改进的密度聚类精确自适应提取LiDAR电力线点云方法  

Improved Density Clustering Method for Accurate Adaptive Extraction of LiDAR Power Line Point Cloud

作　　者：纪凯[1] 武永彩[2] JI Kai;WU Yongcai

机构地区：[1]安徽交通职业技术学院土木工程系,安徽合肥230051 [2]西安思源学院城市建设学院,陕西西安710038

出　　处：《安徽职业技术学院学报》2024年第1期26-30,85,共6页Journal of Anhui Vocational & Technical College

基　　金：2023年度安徽省高等学校自然科学重点研究项目“自建房结构抗震性能及加固研究”(2023AH052965);2022年度安徽高校自然科学研究重点项目“长大水域高铁桥梁摄影测量智能沉降观测技术研究”(2022AH052460);2021年安徽省高校优秀拔尖人才培育项目“高校学科(专业)拔尖人才学术资助项目”(gxbjZD2021127)。

摘　　要：原有邻域半径r_(Eps)与密度阈值p_(MinPts)两个参数的初始赋值导致电力线点云的提取结果存在不确定性,在密度聚类的基础上增添了点云簇类自适应判别方法,该方法避免人员重复测试初始参数的繁琐过程,采用C++语言完成了对该算法电力线精确提取及电力线拟合程序的开发与测试。结果表明:改进后的密度聚类法在电力线点云提取的损失率仅0.02%,三维重建残差为0.213 m;该方法大幅提高了电力线点云提取的准确性与便捷性,适用于高压电力走廊的电力巡检与三维重建等工作。The initial assignment of the two parameters,the original neighborhood radius rEps and density threshold pMinPts,leads to uncertainty in the extraction results of power line point clouds.On the basis of density clustering,an adaptive discrimination method for point cloud cluster classes has been added.This method avoids the tedious process of repeated testing of initial parameters by personnel,and the C++language is used to complete the development and testing of the algorithm for accurate extraction and fitting of power lines.The results show that the improved density clustering method has a loss rate of only 0.02%in extracting power line point clouds,and a residual of 0.213 m in 3D reconstruction;this method greatly improves the accuracy and convenience of extracting power line point clouds and is suitable for power inspection and 3D reconstruction in high-voltage power corridors.

关 键 词：机载LIDAR 点云数据 密度聚类 自适应 三维重建 

分 类 号：P205[天文地球—测绘科学与技术]