Fig. 1. Research flow chart
Fig. 2. Standard normal distribution (Wikipedia, 2005)
Fig. 4. Connected component labeling in 2d space (Wikipedia, 2010)
Fig. 5. PCFA
Fig. 6. Geometric elements of 3d point cloud data using principal component analysis
Fig. 7. Support vector machine
Fig. 8. Data index
Fig. 9. Representation of 3d point cloud data
Fig. 10. Data preprocessing
Fig. 11. Result of PCFA
Fig. 12. Segmentation of road plane objects
Fig. 13. Segmentation of road facility objects
Fig. 14. Pre-processing and ground classification
Fig. 15. Segmentation of road objects
Fig. 16. Result of classification
Fig. 17. Mis-classified objects
Fig. 3. Structure of quadtree and octree Apple developer, 2018)
Table 1. Parameter values for PCFA (unit: m)
Table 2. Training dataset example
Table 3. Confusion matrix of SVM ∙
Table 4. Confusion matrix of object classification
참고문헌
- Apple developer. (2018), Spatial and logical arrangement of an example octree, Apple, URL: https://developer.apple.com/documentation/gameplaykit/gkoctree(last date accessed: 10 November 2018).
- Axelsson, P. (2000), DEM generation from laser scanner data using adaptive TIN models, International Archives of Photogrammetry and Remote Sensing, 16-22 July, Amsterdam, Nederland, Vol. 33, Part B4, pp. 110-117.
- Caputo, M., Denker, K., Franz, M.O., Laube, P., and Umlauf, G. (2014), Support vector machines for classification of geometric primitives in point clouds, Curves and Surfaces, Vol. 9213, pp. 80-95.
- Chang, Y., Habib, A., Lee, D.C., and Yom, J.H. (2008), Automatic classification of LIDAR data into ground and non-ground points, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 3-11 July, Beijing, China, Vol. 37, Part B4, pp. 457-462.
- Han, S.H. (2016), Introduction to Photogrammetry and Remote Sensing, Goomibook, Seoul.
- Hong, S.P. and Kim, E.M. (2017), Object segmentation of laser data using terrestrial mobile mapping system, Proceedings of Journal of Korean Society for Geospatial Information System, Korean Society for Geospatial Information Science, 18-19 May, Jeonju, Korea, pp. 197-198.
- 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.
- 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)
- Kim, E.M. and Cho, D.Y. (2012), Comprehensive comparisons among LIDAR filtering algorithms for the classification of ground and non-ground points, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 30, No. 1, pp. 39-48. (in Korean with English abstract) https://doi.org/10.7848/ksgpc.2012.30.1.039
- Lalonde, J.F., Vandapel, N., Huber, D.F., and Hebert, M. (2006), Natural terrain classification using three-dimensional LIDAR data for ground robot mobility, Journal of Field Robotics, Vol. 23, Issue 1, pp. 839-861. https://doi.org/10.1002/rob.20134
- Lee, G.W. and Son, H.U. (2016), Geo-Spatial Information System, Goomibook, Seoul.
- Lee, J.H. and Lee, D.C. (2010), LIDAR data segmentation using aerial images for building modeling, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 28, No. 1, pp. 47-55. (in Korean with English abstract)
- Lee, S.J., Park, J.Y., and Kim, E.M. (2014), Development of automated model of tree extraction using aerial LIDAR data, Journal of the Korea Academia-Industrial cooperation Society, Vol. 15, No. 5, pp. 3213-3219. (in Korean with English abstract) https://doi.org/10.5762/KAIS.2014.15.5.3213
- 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
- NGII. (2015), A Study on the Construction of Precision Road Map for the Support of Autonomous Vehicle, Research report, National Geographic Information Institute, Korea, pp. 23-93.
- Park, S., Kim, K.J., Lee, J.S., and Lee, S.R. (2011), Red tide prediction using neural network and SVM, The Institute of Electronics Engineers of Korea-Signal Processing, Vol. 48. No. 5, pp. 39-45. (in Korean with English abstract)
- Rusu, R.B. and Cousins, S. (2011), 3D is here: point cloud library (pcl). IEEE International Conference on Robotics and Automation, 9-13 May, Shanghai, China, pp. 1-4.
- 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.
- Sun, Y., Wang, C., Li, J., Zhang, Z., Zai, D., Huang, P., and Wen, C. (2016), Automated segmentation of LIDAR point clouds for building rooftop extraction, IEEE International Geoscience and Remote Sensing Symposium, 10-15 July, Beijing, China, pp. 1472 - 1475.
- Wikipedia. (2005), Normal distribution curve that illustrates standard deviations, Wikimedia Foundation, Inc., URL: https://en.wikipedia.org/wiki/Standard_deviation (last date accessed: 10 November 2018).
- Wikipedia. (2010), Result of connected region labeling using two-pass raster scan, Wikimedia Foundation, Inc., URL: https://en.wikipedia.org/wiki/Connected-component_labeling(last date accessed: 10 November 2018).
- Yoo, H.H., Kim, E.M., and Chung, D.K. (2005), Assessment of classification accuracy of ground and non-ground points from LIDAR data, Journal of The Korean Society of Civil Engineers, Vol. 25, No. 6D, pp. 929-935. (in Korean with English abstract)
- Zhang, K. and Whitman, D. (2005), Comparison of three algorithms for filtering airborne LIDAR data, Photogrammetric Engineering and Remote Sensing, Vol. 71, No. 3, pp. 313-324. https://doi.org/10.14358/PERS.71.3.313
- Zhang, W., Qi, J., Wan, P., Wang, H., Xie, D., Wang., X., and Yan, G. (2016), An easy to use airborne LIDAR data filtering method based on cloth simulation, Remote Sensing, Vol. 8, No. 6, pp. 501-522. https://doi.org/10.3390/rs8060501
피인용 문헌
- 포인트 클라우드에서 딥러닝을 이용한 객체 분류 및 변화 탐지 vol.50, pp.2, 2018, https://doi.org/10.22640/lxsiri.2020.50.2.37
- 정밀도로지도 제작을 위한 모바일매핑시스템 기반 딥러닝 학습데이터의 자동 구축 vol.39, pp.3, 2021, https://doi.org/10.7848/ksgpc.2021.39.3.133