AUTOMATIC 3D POWERLINE RECONSTRUCTION USING AIRBORNE LiDAR DATA

Abstract

The safety of powerline infrastructure significantly affects our everyday life and industrial activities. There are many factors and objects to threaten powerline safety, which include encroaching vegetation, tree healthiness, ambient temperature of the powerlines, structural faults of insulator and tower and so on. A timely and accurate monitoring of those key powerline features enables to prevent causing possible dangerous situation such as blackout. At present, most of utility firms heavily rely on men-centric powerline monitoring methods which are time consuming and very costly, and also, hazardous work. Recently, airborne LiDAR system was introduced as a cost effective data acquisition tool which enables to rapidly capture 3D powerline scene with up to about 30 points/m 2 . This dramatically increased point density would provide a great possibility for achieving the automation of 3D reconstruction of powerline scene features which is an essential step for a machine-based powerline safety monitoring. Since it has been lately used for the powerline monitoring, not many automatic algorithms for reconstructing powerline have been introduced using LiDAR data. This paper introduces a Voxel-based Piece-wise Line Detector (VPLD) which automatically reconstructs 3D powerline models using airborne LiDAR data. The VPLD is developed based on well-known perceptual grouping framework which reconstructs a powerline by grouping similar features at different levels of information (i.e., points, segments and lines). A final reconstruction of single powerline models is accomplished by applying a non-linear adjustment for estimating catenary line parameters to a piece-wisely segmented voxel space. An evaluation of the proposed approach over a complicated powerline scene shows that the proposed method is promising.