An automatic algorithm of power line extraction from LiDAR point cloud

doi:10.13474/j.cnki.11-2246.2021.0172

Abstract

Abstract: Aiming at the problem of low automation and accuracy of current power line extraction methods, this paper proposes an automatic power line extraction method based on the spatial distribution characteristics of point cloud data. Firstly, based on the nature breaks, the point cloud data is classified according to the elevation, and the ground points are removed. Then, the data is partitioned spatially. Based on the point density of subspace and the difference of spatial structure features, the pylon points and residual vegetation points are removed by ground object segmentation algorithm. Finally, the power line automatic detection algorithm based on Euclidean distance is used to realize the fast and high-precision extraction of a single power line.The results of extraction and fitting experiments show that this method can automatically extract power lines in complex terrain, and greatly improve the efficiency and accuracy of power line extraction.

Key words: airborne LiDAR point cloud, power line extraction, segment statistics, Euclidean distance segmentation, automatic detection

CLC Number:

P237

LI Deyou, LI Cailin, LI Xiangshen, WANG Baitao. An automatic algorithm of power line extraction from LiDAR point cloud[J]. Bulletin of Surveying and Mapping, 2021, 0(6): 33-38.

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