DOI QR코드

DOI QR Code

정밀도로지도 레이어의 품질향상을 위한 자동오류 판독 연구

Study on Automated Error Detection Method for Enhancing High Definition Map

  • Hong, Song Pyo (Spatial Information Industry Promotion Institute) ;
  • Oh, Jong Min (Department of Geoinformatics Engineering, University of Seoul, Spatial Information Industry Promotion Institute) ;
  • Song, Yong Hyun (Spatial Information Industry Promotion Institute) ;
  • Shin, Young Min (Department of Geoinformatics Engineering, University of Seoul, Spatial Information Industry Promotion Institute) ;
  • Sung, Dong Ki (Spatial Information Industry Promotion Institute)
  • 투고 : 2020.08.03
  • 심사 : 2020.08.21
  • 발행 : 2020.08.31

초록

급변하는 주변상황이나 대형차량과 같은 큰 지형지물에 센서가 가려질 경우에는 센서만을 이용한 완전 자율주행에는 한계가 따른다. 이에 자율주행을 위해서 센서를 이용한 한계점을 극복할 수 있도록 정밀한 도로지도를 부가적으로 이용하는 방법이 사용되고 있으며, 국내의 경우에는 국토지리정보원에서 자율주행차를 위한 정밀한 도로지도를 제작 및 공급하고 있다. 이에 본 연구에서는 국토지리정보원에서 제공하는 정밀도로지도의 정위치 및 구조화 편집 과정에서 발생하는 오류를 체계적으로 유형화 하였다. 또한 각 상황에 맞는 오류 탐색 과정 및 해결방안을 제시하여 신속하게 정밀도로지도의 오류를 수정할 수 있는 연구를 수행하였으며 크게 도형무결성, 공간관계, 참조관계에 대하여 오류항목을 대분류하고 이에 대한 세부적인 검사방법을 도출하였다.

Autonomous driving can be limited by only using sensors if the sensor is blocked by sudden changes in surrounding environments or large features such as heavy vehicles. In order to overcome the limitations, the precise road-map has been used additionally. In korea, the NGII (National Geographic Information Institute) produces and supplies high definition map for autonomous vehicles. Accordingly, in this study, errors occurring in the process of e data editing and dtructured esditing of high definition map are systematically typed providing by the National Geographic Information Institute. In addition, by presenting the error search process and solution for each situation, we conducted a study to quickly correct errors in high definition map, and largely classify the error items for shape integrity, spatial relationship, and reference relationship, and examine them in detail. The method was derived.

키워드

참고문헌

  1. Choi, J.S. (2015), Improving the Inspection Process of Base Surveying For National Base Map's Quality Enhancement, Master's thesis, University of Seoul, Seoul, Korea, 82p.
  2. Clementini, E., Sharma, J., and Egenhofer, M.J. (1994), Modelling topological spatial relations: Strategies for query processing. Computers and Graphics, Vol. 18, No. 6, pp. 815-822. https://doi.org/10.1016/0097-8493(94)90007-8
  3. Hong, S.P. and Kim, E.M. (2019), Classification of 3d road objects using machine learning, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 36, No. 6, pp. 535-544. (in Korean with English abstract) https://doi.org/10.7848/KSGPC.2018.36.6.535
  4. Hong, S.P., Seo, H.D., and Kim, E.M. (2018), Road object classification using a terrestrial laser data, Proceedings of Journal of Korean Society for Geospatial Information System, Korean Society for GeoSpatial Information Science, 1-2 November, Jeju, Korea, pp. 199-200.
  5. Jeong, J.H. and Lee, I.P. (2016), Classification of mobile LIDAR data acquired from urban roads based on eigenvalue ratios and support vector machine, Journal of the Korean Cadastre Information Association, Vol. 18, No. 2, pp. 195-206, (in Korean with English abstract)
  6. KAIA. (2017), Development and Verification of Rapid Detection Technology of Road Change for Supporting Autonomous Driving, pp. 14-65.
  7. Lee, J.K, Lee, D.J., and Jung, S.H. (2008), Development of digital map on-demand updating system, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 26, No. 5, pp. 537-546. (in Korean with English abstract)
  8. Lee, J.S., Park, C.T., and Park, K.S. (2005), Advanced process technique for field check data editing and structured editing on digital map ver2.0 applying automatic error detection method, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 23, No. 3, pp. 331-340. (in Korean with English abstract)
  9. Lehtomaki, M., Jaakkola, A., Hyyppa, J., Lampinen, J., Kaartinen, H., Kukko, A., Puttonen, E., and Hyyppa, H. (2015), Object classification and recognition from mobile laser scanning point clouds in a road environment, IEEE Transactions on Geoscience and Remote Sensing, Vol. 54, No. 2, pp. 1226-1239. https://doi.org/10.1109/TGRS.2015.2476502
  10. NGII. (2015), A Study on the Construction of HD Map for the Support of Autonomous Vehicle, pp. 33-71.
  11. NGII. (2019), Hd map construction manual, URL: https://www.ngii.go.kr/kor/contents/view.do?sq=1195&board_code=contents_data (last date accessed: 10 July 2020).
  12. Park, C.T. (2006), Study on Automated Error Detection Method for Enhancing Ver2.0 Digital Topographic, Maps, Master's thesis, Inha University, Incheon, Korea, 54p.
  13. Qi, C.R., Su, H., Mo, K., and Guibas, L.J. (2016), Pointnet: deep learning on point sets for 3d classification and segmentation, In Proceedings of the 2016 Conference on Computer Vision and Pattern Recognition, 26-1 July, Las Vegas, NV, USA, pp. 652-660.
  14. Qi, H. (2009), Detection and Generalization of Changes in Settlements for Automated Digital Map Updating, Ph.D. dissertation, Hong Kong Polytechnic University, Hong Kong, China, 165p.
  15. So, J.H. and Moon, Y.J. (2018), Plan for autonomous cooperation driving safety and infrastructure implementation, The Journal of The Korean Institute of Communication Sciences, Vol. 35, No. 5, pp. 37-43.
  16. Wikipedia. (2020), DE-9IM, Wikimedia Foundation, Inc., URL: https://https://en.wikipedia.org/wiki/DE-9IM (last date accessed: 10 August 2020)