一种基于无人机LiDAR点云的电力线快速提取方法  

A method of power line fast extraction based on UAV LiDAR point cloud

作　　者：王汉顺 WANG Hanshun(Xiamen MinKuang Survering&Mapping Institute,Xiamen,Fujian 361004,China)

机构地区：[1]厦门闽矿测绘院,福建厦门361004

出　　处：《测绘标准化》2024年第1期166-170,共5页Standardization of Surveying and Mapping

摘　　要：为了改变传统人力巡检长距离电力线效率低下的问题,提高生产效率,满足电网智能化管理需求,本文基于机载激光雷达(LiDAR)点云数据,采用凝聚层次聚类算法,利用C语言编程,实现了对电力线点云的快速和精确提取,并通过实例进行验证。结果表明,凝聚层次聚类算法对电力线点云提取的准确率达99.99%,避雷线最小拟合残差为0.11 m,电力线最小拟合残差为0.19 m,最大拟合残差为0.21 m,平均拟合残差为0.20 m。该算法的自动化实现过程,可提高电力线的巡检效率,获取较好的测试效果。In order to change the drawbacks of low efficiency in traditional manual inspection of long-distance power lines,improve production efficiency,and meet the needs of intelligent management of power grids,this paper is based on airborne LiDAR point cloud data,adopts the agglomerative clustering algorithm,uses C language programming to achieve rapid and accurate extraction of power line point clouds,and verifies through examples.The results show that the accuracy of the agglomerative clustering algorithm in extracting power line point clouds is 99.99%,the minimum fitting residual of the lightning line is 0.11 m,the minimum fitting residual of the power line is 0.19 m,the maximum fitting residual is 0.21 m,and the average fitting residual is 0.20 m.The automated implementation process of the algorithm can improve the inspection efficiency of power lines and obtain better test results.

关 键 词：LIDAR点云 凝聚层次聚类 电力线 三维重建 精度评定 

分 类 号：P205[天文地球—测绘科学与技术]