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

  • 제39권1호
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  • Pages.55-63
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  • 2021
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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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드론 라이다와 영상에 의한 포장 노면의 평탄성 분석

Roughness Analysis of Paved Road using Drone LiDAR and Images

  • Jung, Kap Yong (Department of Construction Engineering Education, Chungnam National University) ;
  • Park, Joon Kyu (Department of Civil Engineering, Seoil University)
  • 투고 : 2021.01.25
  • 심사 : 2021.02.24
  • 발행 : 2021.02.28
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초록

도로의 평탄성은 승차감과 직결되는 중요한 요소이며, 도로의 기능평가 및 포장품질 관리를 위한 평가항목이다. 본 연구에서는 지상 LiDAR, 드론 사진측량 및 드론 LiDAR의 최신 3차원 공간정보 구축 기술을 활용하여 도로 노면에 대한 데이터를 취득하고, 각각의 방법에 대한 정확도 및 평탄성을 분석하였다. 정확도 평가 결과 지상 LiDAR는 X, Y, Z 방향으로 각각 0.039m, 0.042m, 0.039m의 RMSE를 나타내었으며 드론 사진측량과 드론 LiDAR는 각각 0.072~0.076m, 0.060~0.068m의 RMSE를 나타내어 측량 및 지도제작 분야에 각각의 방법이 충분히 활용 가능함을 제시하였다. 또한 편평도 분석을 위해 각각의 방법으로 구축된 3차원 공간정보에서 대상 구간에 대한 종방향 경사와 횡방향 경사를 추출하고, 설계값과 비교를 수행하였다. 평탄성 분석 결과 지상 LiDAR는 설계값과 동일한 경사를 나타내었으며, 드론 사진측량 및 드론 LiDAR의 경우 설계값과 다소 차이를 보였다. 도로의 평탄성 분석과 같은 계측 분야에 드론 사진측량 및 드론 LiDAR를 활용하기 위한 정확도를 향상 방안 연구가 필요하다. 향후 정확도 향상을 통한 활용성을 제시할 수 있다면 드론 사진측량 및 드론 LiDAR를 활용하여 취득에 소요되는 시간을 크게 감소시킬 수 있으므로 관련 업무 효율성 향상이 가능할 것이다.

The roughness of the road is an important factor directly connected to the ride comfort, and is an evaluation item for functional evaluation and pavement quality management of the road. In this study, data on the road surface were acquired using the latest 3D geospatial information construction technology of ground LiDAR, drone photogrammetry, and drone LiDAR, and the accuracy and roughness of each method were analyzed. As a result of the accuracy evaluation, the average accuracy of terrestrial LiDAR were 0.039m, 0.042m, 0.039m RMSE in X, Y, Z direction, and drone photogrammetry and drone LiDAR represent 0.072~0.076m, 0.060~0.068m RMSE, respectively. In addition, for the roughness analysis, the longitudinal and lateral slopes of the target section were extracted from the 3D geospatial information constructed by each method, and the design values were compared. As a result of roughness analysis, the ground LiDAR showed the same slope as the design value, and the drone photogrammetry and drone LiDAR showed a slight difference from the design value. Research is needed to improve the accuracy of drone photogrammetry and drone LiDAR in measurement fields such as road roughness analysis. If the usability through improved accuracy can be presented in the future, the time required for acquisition can be greatly reduced by utilizing drone photogrammetry and drone LiDAR, so it will be possible to improve related work efficiency.

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