Journal of the Korean Association of Geographic Information Studies (한국지리정보학회지)

The Korean Association of Geographic Information Studies (한국지리정보학회)
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Method to Improve the Location Accuracy of GPR Data for Underground Information Precise Detecting

지하정보 정밀탐사를 위한 GPR 데이터 위치정확도 개선 방안

  • RYU, Jisong (Korea Institute of Civil engineering and Building Technology) ;
  • JANG, Yonggu (Korea Institute of Civil engineering and Building Technology) ;
  • PARK, Donghyun (Korea Institute of Civil engineering and Building Technology)
  • Received : 2021.08.02
  • Accepted : 2021.08.19
  • Published : 2021.09.30
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Abstract

Underground information is difficult to visually check, which can lead to a huge accident in the event of a safety accident. Recently, the Ministry of Land, Infrastructure and Transport intends to reduce safety accidents caused by the aging or damage of underground facilities through the Special Act on Underground Safety Management. GPR is increasingly being used as a technology to acquire information in underground spaces that are difficult to see with the naked eye. However, GPR's location information is corrected by checking images of CCTV and GPS information acquired during exploration. This method has an average error of about 2 meters. In this works, We used LiDAR to calibrate the GPR information and found that the error was reduced from at least 7cm to up to 40cm. If accurate GPR information collected in the future is analyzed quickly using AI, etc., it will be able to collect and utilize underground information faster than it is now to secure safety.

지하정보는 육안으로 확인이 어려워 안전사고가 발생할 경우 큰 사고로 이어질 수 있다. 최근 국토교통부는 「지하안전관리에 관한 특별법」 재정을 통해 지하매설물의 노후화 또는 파손으로 인해 발생하는 안전사고를 줄이고자 한다. GPR은 육안으로 확인이 어려운 지하공간의 정보를 습득하는 기술로 활용이 많아지고 있다. 그러나 GPR의 위치정보는 탐사 중 습득된 GPS 정보와 영상을 확인하여 보정한다. 이 방식은 평균 오차가 2m 정도 발생한다. 따라서 평면오차를 감소시킬 방안으로 LiDAR를 통한 보정법을 제시했다. 또한 제시된 방법을 활용하여 GPR정보를 보정하였다. 그 결과 오차가 최소 7cm에서 최대 40cm 수준으로 감소하는 것을 볼 수 있었다. 향후 수집된 정확도 높은 GPR 정보를 AI 등을 활용하여 신속하게 분석한다면 현재보다 더 빠르게 지하정보를 수집하고 활용하여 안전을 확보할 수 있을 것이다.

Keywords

Acknowledgement

본 연구는 국토교통부/국토교통과학기술진흥원의 지원으로 수행되었음(과제번호 20DCRU-B158151-01)

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