Extraction of power lines from laser point cloud based on residual clustering method

被引：0

作者：

Ma W. ^{[1
,2
,3
]}

Wang C. ^{[1
,4
]}

Wang J. ^{[1
,2
,3
]}

Zhou J. ^{[1
]}

Ma Y. ^{[5
]}

机构：

[1] College of Tourism and Geographic Sciences, Yunnan Normal University, Kunming

[2] Key Laboratory of Resources and Environmental Remote Sensing for Universities in Yunnan, Kunming

[3] Center for Geospatial Information Engineering and Technology of Yunnan Province, Kunming

[4] Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, China Academy of Sciences, Beijing

[5] Chinese Antarctic Center of Surveying and Mapping, Wuhan university, Wuhan

来源：

Cehui Xuebao/Acta Geodaetica et Cartographica Sinica | 2020年 / 49卷 / 07期

基金：

中国国家自然科学基金;

关键词：

Density clustering; Model reconstruction; Model residual; Point cloud data; Power line extraction;

D O I：

10.11947/j.AGCS.2020.20190373

中图分类号：

学科分类号：

摘要：

Aiming at the complex environment such as missing and noise in power line cloud data, a precise power line extraction method based on model residual clustering from LiDAR point is proposed. Firstly, the near-ground points are removed according to the normalized elevation threshold segmentation. The power line points are roughly extracted using adaptive dimension features and directional features. Secondly, the improved modeling method is adopted to determine the model residual error with the constraint condition of the parabolic model. The result obtained by density clustering on the model residual error is used to extract the single power line point. Finally, the influence of the selection of key parameters on the extraction results is discussed. Two experimental results show that the method can quickly extract power line from point cloud with partial missing and noise interference, without prior knowledge such as the number of power lines and density of point cloud, etc. Which has good applicability for different types of bundle conductor extraction. the accuracy of single power line extraction is more than 99.17%, the maximum error of model fitting is 0.167 m, and the maximum mean square error of model fitting is 0.079 m. © 2020, Surveying and Mapping Press. All right reserved.

引用

页码：883 / 892

页数：9

共 28 条

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