Abstract
Abstract. Research on power line extraction technology using mobile laser point clouds has important practical significance on railway power lines patrol work. In this paper, we presents a new method for automatic extracting railway power line from MLS (Mobile Laser Scanning) data. Firstly, according to the spatial structure characteristics of power-line and trajectory, the significant data is segmented piecewise. Then, use the self-adaptive space region growing method to extract power lines parallel with rails. Finally use PCA (Principal Components Analysis) combine with information entropy theory method to judge a section of the power line whether is junction or not and which type of junction it belongs to. The least squares fitting algorithm is introduced to model the power line. An evaluation of the proposed method over a complicated railway point clouds acquired by a RIEGL VMX450 MLS system shows that the proposed method is promising.
Highlights
Railway power supply security is closely related to our everyday life and industrial activities
Research on mobile laser point cloud data in power line extraction technology has important practical significance on railway power lines patrol work. (Lehtomaki et al, 2010) proposed an automatic method to extract pole-like objects from MLS data. (Cheng et al, 2014) presented a method to extract power line points using a voxel-based hierarchical method, and the point densities of neighboring voxels are calculated for further filtering. (Guan et al, 2014) proposed to extract power-line points in the identified non-road points, followed by Hough transform and Euclidean distance clustering
There two defects exist in airborne laser scanning system lead to it impossible implement very well in railway power line detection
Summary
Many research works have been carrying out using Airborne LiDAR data for automatic detection and extraction of power lines. They applied many valid methods to reconstruct 3D power lines using airborne LiDAR point clouds such as the Random Sample Consensus (RANSAC) method (Chen et al, 2012), Markov Random Field (MRF) classifier (Sohn et al, 2012), Hough Transform method (Liu et al, 2009) and Random Forests classification method (Kim and Sohn, 2013). We propose a stepwise method to extract the power line feasible and fast enough This method use spatial information between power line and trajectory to separate a segment non-road points from original point cloud data.
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