KSCE Journal of Civil and Environmental Engineering Research (대한토목학회논문집)

Korean Society of Civil Engeneers (대한토목학회)
권호 표지
DOI QR코드

DOI QR Code

Development of Registration Post-Processing Technology to Homogenize the Density of the Scan Data of Earthwork Sites

토공현장 스캔데이터 밀도 균일화를 위한 정합 후처리 기술 개발

  • Kim, Yonggun (Korea National University of Transportation) ;
  • Park, Suyeul (Korea National University of Transportation) ;
  • Kim, Seok (Korea National University of Transportation)
  • 김용건 (한국교통대학교 철도시설공학과) ;
  • 박수열 (한국교통대학교 철도융합시스템공학과) ;
  • 김석 (한국교통대학교 철도인프라시스템공학과)
  • Received : 2022.03.03
  • Accepted : 2022.07.25
  • Published : 2022.10.01
Download PDF
⟨ Previous Next ⟩

Abstract

Recently, high productivity capabilities have been improved due to the application of advanced technologies in various industries, but in the construction industry, productivity improvements have been relatively low. Research on advanced technology for the construction industry is being conducted quickly to overcome the current low productivity. Among advanced technologies, 3D scan technology is widely used for creating 3D digital terrain models at construction sites. In particular, the 3D digital terrain model provides basic data for construction automation processes, such as earthwork machine guidance and control. The quality of the 3D digital terrain model has a lot of influence not only on the performance and acquisition environment of the 3D scanner, but also on the denoising, registration and merging process, which is a preprocessing process for creating a 3D digital terrain model after acquiring terrain scan data. Therefore, it is necessary to improve the terrain scan data processing performance. This study seeks to solve the problem of density inhomogeneity in terrain scan data that arises during the pre-processing step. The study suggests a 'pixel-based point cloud comparison algorithm' and verifies the performance of the algorithm using terrain scan data obtained at an actual earthwork site.

최근 다양한 산업에서 첨단기술의 적용으로 높은 생산성 향상을 이루고 있지만 건설산업의 경우 생산성 향상이 비교적 낮게 조사되어, 이를 극복하기 위한 첨단기술 연구가 빠르게 진행되고 있다. 여러 첨단기술 중 3차원 스캔 기술은 실제 대상물을 손쉽게 디지털화 할 수 있다는 점에서 건설현장의 3차원 디지털 지형 모델 생성을 위한 기술로 널리 활용되고 있다. 특히 3차원 디지털 지형 모델은 토공 중장비의 자동제어 및 가이던스 등과 같은 건설 자동화의 기초자료가 될 수 있어 지형 스캔데이터의 높은 품질이 요구되고 있다. 3차원 디지털 지형 모델의 품질은 3D 스캐너의 성능 및 취득환경뿐 아니라 지형 스캔데이터 취득 후 3차원 디지털 지형 모델 생성을 위한 전처리 과정인 노이즈제거, 정합 및 병합과정 등 또한 많은 영향을 끼치고 있어, 지형 스캔데이터 처리의 성능 증진이 필요할 것으로 보인다. 본 연구에서는 3차원 디지털 지형 모델 생성을 위한 전처리 과정 중 정합과정에서 발생하는 지형 스캔데이터의 밀도 불균일 문제를 해결하고자 한다. 이를 위해 본 연구에서 개발한 정합 후처리 기술인 '픽셀기반 점군비교 알고리즘'을 제시하였으며, 실제 토공현장에서 취득한 지형 스캔데이터를 활용해 개발한 알고리즘의 성능검증을 수행하여 지형 스캔데이터 정합 후 불균일 문제의 개선 가능성을 검증하고 밀도 별 지형 스캔데이터에 대한 알고리즘의 최적 파라미터를 제시하였다.

Keywords

Acknowledgement

본 연구는 국토교통부/국토교통과학기술진흥원이 시행하는 "스마트건설기술개발 국가R&D사업(과제번호 21SMIP-A158708-02)"의 지원으로 수행하였습니다.

References

  1. Bello, S. A., Yu, S., Wang, C., Adam, J. M. and Li, J. (2020). "Deep learning on 3D point clouds." Remote Sensing, Vol. 12, No. 11, DOI: 10.3390/rs12111729.
  2. Cheng, L., Chen, S., Liu, X., Xu, H., Wu, Y., Li, M. and Chen, Y. (2018). "Registration of laser scanning point clouds: A review." Sensors, Vol. 18, No. 5, 1641. DOI: 10.3390/s18051641.
  3. Elseberg, J., Magnenat, S., Siegwart, R. and Nuchter, A. (2012). "Comparison of nearest-neighbor-search strategies and implementations for efficient shape registration." Journal of Software Engineering for Robotics, Vol. 3, No. 1, pp. 2-12.
  4. Finkel, R. A. and Bentley, J. L. (1974). "Quad trees a data structure for retrieval on composite keys." Acta Informatica, Vol. 4, No. 1, pp. 1-9. https://doi.org/10.1007/BF00288933
  5. Fuad, N. A., Yusoff, A. R., Ismail, Z. and Majid, Z. (2018). "Comparing the performance of point cloud registration methods for landslide monitoring using mobile laser scanning data." ISPRS International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 42 No. 4, pp. 11-21.
  6. Garstka, J. and Peters, G. (2015). "Fast and robust keypoint detection in unstructured 3-D point clouds." International Conference on Informatics in Control, Automation and Robotics (ICINCO), Vol. 2, Colmar, France, pp. 131-140.
  7. Gries, D. and Schneider, F. B. (2013). A logical approach to discrete math, Springer Science & Business Media, New York, USA.
  8. Jaccard, P. (1912). "The distribution of the flora in the alpine zone." New Phytologist, Vol. 11, No. 2, pp. 37-50. https://doi.org/10.1111/j.1469-8137.1912.tb05611.x
  9. Kim, S. and Park, J. W. (2015). "Analysis of accuracy and productivity of terrestrial laser scanner for earthwork." The Journal of the Korea Contents Association, Vol. 15, No. 10, pp. 587-596 (in Korean). https://doi.org/10.5392/JKCA.2015.15.10.587
  10. Kim, S., Park, J. W. and Kim, K. H. (2017). "A study on terrain digitalization for earthwork automation." In Proceedings of the Korea Contents Association Conference, pp. 407-408 (in Korean).
  11. Lee, O. G. and Sim, J. Y. (2021). "Lidar based point cloud acquisition and preprocessing." Broadcasting and Media Magazine, Vol. 26, No. 2, pp. 9-17 (in Korean).
  12. Mckinsey Global Institute (2017). Reinventing construction: A route to higher productivity, New York, USA.
  13. Orts-Escolano, S., Morell, V., Garcia-Rodriguez, J. and Cazorla, M. (2013). "Point cloud data filtering and downsampling using growing neural gas." International Joint Conference on Neural Networks (IJCNN), Texas, USA, pp. 1-8.
  14. Park, J. W. and Kim, S. (2019). "MMS accuracy analysis for earthwork site application." Journal of the Korean Society of Industry Convergence, Vol. 22, No. 2, pp. 183-189 (in Korean). https://doi.org/10.21289/KSIC.2019.22.2.183
  15. Park, S. Y. and Kim, S. (2021). "Analysis of overlap ratio for registration accuracy improvement of 3D point cloud data at construction sites." Journal of KIBIM, Vol. 11, No. 4, pp. 1-9 (in Korean). https://doi.org/10.13161/KIBIM.2021.11.4.001
  16. Tanimoto, T. T. (1958). An elementary mathematical theory of classification and prediction, Internal Report IBM Corp, New York, USA.
  17. Wang, C. and Cho, Y. K. (2015). "Smart scanning and near real-time 3D surface modeling of dynamic construction equipment from a point cloud." Automation in Construction, Vol. 49, pp. 239-249, DOI: 10.1016/j.autcon.2014.06.003.
  18. Wang, Q. and Kim, M. K. (2019). "Applications of 3D point cloud data in the construction industry: A fifteen-year review from 2004 to 2018." Advanced Engineering Informatics, Vol. 39, pp. 306-319, DOI: 10.1016/j.aei.2019.02.007.