한국측량학회지 (Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography)

  • 제31권4호
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  • Pages.331-339
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  • 2013
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  • 1598-4850(pISSN)
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  • 2288-260X(eISSN)
한국측량학회 (Korean Society of Surveying, Geodesy, Photogrammetry and Cartography)
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LiDAR 데이터와 RANSAC 알고리즘을 이용한 철도 전력선 자동탐지에 관한 연구

A Study on the Automatic Detection of Railroad Power Lines Using LiDAR Data and RANSAC Algorithm

  • Jeon, Wang Gyu (Department of Civil & Environmental Engineering, Incheon National University) ;
  • Choi, Byoung Gil (Department of Civil & Environmental Engineering, Incheon National University)
  • 투고 : 2013.04.01
  • 심사 : 2013.08.30
  • 발행 : 2013.08.31
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초록

LiDAR 측량은 고밀도로 정확하게 거리를 측정하는 장점 때문에 지표면과 지표면 위의 객체를 3D 모델링하는데 사용되는 주요기술 중의 하나이다. 본 연구의 목적은 고밀도 LiDAR 데이터와 RANSAC 알고리즘을 이용하여 자동으로 철도전력선을 탐지하고 모델링하는 방법을 개발하는데 있다. 철도전력선을 탐지하기 위하여 레이저 데이터의 다중반사 특성과 철도전력선에 대한 형상정보를 이용한다. 이를 위한 프로세스는 최초 단위라인을 찾기 위한 직육면체 분석과 라인 추적, 연결 그리고 색인 작업으로 구성되며, 반복 RANSAC과 라인 파라미터를 구하기 위한 최소제곱법이 모델링을 위하여 사용된다. 철도전력선의 경우에는 정확도 확인을 위한 실측자료를 구하는 것이 매우 힘들어서 정량적인 정확도 평가가 어려우나 모델에 대한 레이저점군의 표준편차는 x-y 및 z 좌표 각각 8cm와 5cm로 양호하였고, 육안 검사에 의한 완성도면에서도 원 데이터와 비교할 때 모든 철도전력선 라인이 탐지 및 모델링된 것을 알 수 있었다. 본 연구에서 제시하는 방법의 모든 과정은 완전히 자동화하였으며, 특히 다수의 전력선이 복잡하게 설치된 지역에서도 적용될 수 있도록 개발하였다.

LiDAR has been one of the widely used and important technologies for 3D modeling of ground surface and objects because of its ability to provide dense and accurate range measurement. The objective of this research is to develop a method for automatic detection and modeling of railroad power lines using high density LiDAR data and RANSAC algorithms. For detecting railroad power lines, multi-echoes properties of laser data and shape knowledge of railroad power lines were employed. Cuboid analysis for detecting seed line segments, tracking lines, connecting and labeling are the main processes. For modeling railroad power lines, iterative RANSAC and least square adjustment were carried out to estimate the lines parameters. The validation of the result is very challenging due to the difficulties in determining the actual references on the ground surface. Standard deviations of 8cm and 5cm for x-y and z coordinates, respectively are satisfactory outcomes. In case of completeness, the result of visual inspection shows that all the lines are detected and modeled well as compare with the original point clouds. The overall processes are fully automated and the methods manage any state of railroad wires efficiently.

키워드

참고문헌

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피인용 문헌

  1. 비행장애물 회피를 위한 라이다 기반 송전선 고속탐지 및 적용가능성 분석 vol.22, pp.1, 2013, https://doi.org/10.12672/ksis.2014.22.1.075
  2. Development of Classification Technique of Point Cloud Data Using Color Information of UAV Image vol.35, pp.4, 2013, https://doi.org/10.7848/ksgpc.2017.35.4.303
  3. Review of Laser Scanning Technologies and Their Applications for Road and Railway Infrastructure Monitoring vol.4, pp.4, 2013, https://doi.org/10.3390/infrastructures4040058
  4. 딥 러닝과 파노라마 영상 스티칭 기법을 이용한 송전선 늘어짐 모니터링 시스템 vol.25, pp.1, 2013, https://doi.org/10.5909/jbe.2020.25.1.13