DOI QR코드

DOI QR Code

A Study on Position Matching Technique for 3D Building Model using Existing Spatial Data - Focusing on ICP Algorithm Implementation -

기구축 공간데이터를 활용한 3차원 건물모델의 위치정합 기법 연구 - ICP 알고리즘 구현 중심으로 -

  • Lee, Jaehee (LX Spatial Information Research Institute) ;
  • Lee, Insu (LX Spatial Information Research Institute) ;
  • Kang, Jihun (LX Spatial Information Research Institute)
  • Received : 2021.05.04
  • Accepted : 2021.06.28
  • Published : 2021.06.30

Abstract

Spatial data is becoming very important as a medium that connects various data produced in smart cities, digital twins, autonomous driving, smart construction, and other applications. In addition, the rapid construction and update of spatial information is becoming a hot topic to satisfy the diverse needs of consumers in this field. This study developed a software prototype that can match the position of an image-based 3D building model produced without Ground Control Points using existing spatial data. As a result of applying this software to the test area, the 3D building model produced based on the image and the existing spatial data show a high positional matching rate, so that it can be widely used in applications requiring the latest 3D spatial data.

최근 스마트시티, 디지털 트윈, 자율주행, 스마트 건설 등의 분야가 발전하면서 각 분야에서 생산되는 다양한 데이터를 서로 연결하기 위한 매개체로서 공간정보의 가치가 매우 중요해지고 있다. 특히 데이터의 최신성을 위해 공간정보의 신속한 구축과 갱신이 필요하다. 본 연구에서는 정밀한 위치정확도가 이미 검증된 기구축 공간데이터를 이용하여 지상기준점 없이 제작된 영상기반 3차원 건물모델의 위치를 정합시킬 수 있는 소프트웨어 프로토타입을 개발하였다. 이 소프트웨어를 실험대상지역에 적용한 결과, 지상기준점 없이 제작된 3차원 건물모델과 기구축 공간데이터가 높은 위치 정합률을 보여 최신의 3차원 공간데이터를 필요로 하는 분야에 적용될 수 있음을 확인하였다.

Keywords

Acknowledgement

본 연구는 국토교통부 수요처 맞춤형 실감형 3D 공간정보 갱신 및 활용지원 기술개발 과제의 연구비 지원(21DRMS-B147287-04)에 의해 수행되었습니다.

References

  1. Kim MS, Park DY. 2020. A Study on Feasible 3D Object Model Generation Plan Based on Utilization, Demand, and Generation Cost. Journal of Cadastre & Land InformatiX. 50(1):215-229. https://doi.org/10.22640/LXSIRI.2020.50.1.215
  2. Kim JG, Lee JH, Park SM, Ko KH. 2018. A Modified Method for Registration of 3D Point Clouds with a Low Overlap Ratio. Journal of the Korea Computer Graphics Society. 24(5):11-19. https://doi.org/10.15701/KCGS.2018.24.5.11
  3. Kim TH, Lee YC. 2020. Comparison of Open Source based Algorithms and Filtering Methods for UAS Image Processing. Journal of Cadastre & Land InformatiX. 50(2):155-168. https://doi.org/10.22640/lxsiri.2020.50.2.155
  4. Kim HG, Hwang YH, Rhee SA. 2019. Automatic Building Modeling Method Using Planar Analysis of Point Clouds from Unmanned Aerial Vehicles. Korean Journal of Remote Sensing. 35(6-1):973-985.
  5. Kim HG, Hwang YH, Rhee SA. 2020. Automatic Generation of Clustered Solid Building Models Based on Point Cloud. Korean Journal of Remote Sensing. 36(6-1):1349-1365.
  6. Seo HD, Kim EM. 2020. Object Classification and Change Detection in Point Clouds Using Deep Learning. Journal of Cadastre & Land InformatiX. 50(2):37-51. https://doi.org/10.22640/lxsiri.2020.50.2.37
  7. Yun BY, Lee JO, Lee DS. 2016. A Study on the Enactment of UAV Standard Estimating for Applying in Spatial Information Area. Journal of The Korean Cadastre Information Association. 18(1):123-132.
  8. Lee JO, Sung SM. 2018. Assessment of Positioning Accuracy of UAV Photogrammetry based on RTK-GPS. Journal of The Korea Academia-Industrial cooperation Society. 19(4):63-68. https://doi.org/10.5762/KAIS.2018.19.4.63
  9. Lim SH, Choi KM, Cho GS. 2020. A Study on 3D Model Building of Drones-Based Urban Digital Twin. Journal of Cadastre & Land InformatiX. 50(1):163-180. https://doi.org/10.22640/LXSIRI.2020.50.1.163
  10. Han SH, Jung KY. 2015. Accuracy Evaluation of UAV based on GCP Usage. Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography. 255-256.
  11. Han SH. 2019. Project Design Plan for Drone Photogrammetry. Journal of the Korean Society of Civil Engineers. 39(1):239-246. https://doi.org/10.12652/KSCE.2019.39.1.0239
  12. Deng F. 2011. Registration between Multiple Laser Scanner Data Sets. Laser Scanning, Theory and Applications:IntechOpen, p. 449-472.
  13. Gustafsson H, Zuna L. 2017. Unmanned Aerial Vehicles for Geographic Data Capture: A Review[Bachelor thesis]. Royal Institute of Technology. p.47-54.
  14. Khoramshahi E, Oliveira RA, Koivumaki N, Honkavaara E. 2020. An Image-Based Real-Time Georeferencing Scheme for a UAV Based on a New Angular Parametrization. Remote Sensing. 12(19):3185-3211. https://doi.org/10.3390/rs12193185
  15. Koeva M, Muneza M, Gevaert C, Gerke M, Nex F. 2016. Using UAVs for map creation and updating. A case study in Rwanda. Survey Review. 50:312-325. https://doi.org/10.1080/00396265.2016.1268756
  16. Penney GP, Edwards PJ, King AP, Blackall JM, Batchelor PG, Hawkes DJ. 2001. A Stochastic Iterative Closest Point Algorithm (stochast-ICP). Springer, Berlin, Heidelberg; p. 762-769.
  17. Pix4Dmapper. 2017. Do RTK/PPK drones give you better results than GCPs? [Internet]. [https://www.pix4d.com/blog/rtk-ppk-drones-gcp-comparison]. Last accessed 03 May 2021.
  18. Rusinkiewicz S, Levoy, M. 2001. Efficient variants of the ICP algorithm. In Proceedings of Proceedings Third International Conference on 3-D Digital Imaging and Modeling. p. 145-152.
  19. Rusu RB, Blodow N, Beetz M. 2009. Fast Point Feature Histograms (FPFH) for 3D registration. In Proceedings of 2009 IEEE International Conference on Robotics and Automation. p. 3212-3217.
  20. Zhang H, Aldana-Jague E, Clapuyt F, Wilken F, Vanacker V, Oost KV. 2019. Evaluating the potential of post-processing kinematic(PPK) georeferencing for UAV-based structure-from-motion(SfM) photogrammetry and surface change detection. Earth Surface Dynamics. 7(3):807-827. https://doi.org/10.5194/esurf-7-807-2019