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http://dx.doi.org/10.12652/Ksce.2022.42.5.0689

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

Kim, Yonggun (Korea National University of Transportation)
Park, Suyeul (Korea National University of Transportation)
Kim, Seok (Korea National University of Transportation)
Publication Information
KSCE Journal of Civil and Environmental Engineering Research / v.42, no.5, 2022 , pp. 689-699 More about this Journal
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.
Keywords
3D digital terrain model; Terrain scan data; Earthwork site; Registration; Post-processing technology;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
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1 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.
2 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).
3 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).
4 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).   DOI
5 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.   DOI
6 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.   DOI
7 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.   DOI
8 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.
9 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.
10 Jaccard, P. (1912). "The distribution of the flora in the alpine zone." New Phytologist, Vol. 11, No. 2, pp. 37-50.   DOI
11 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).   DOI
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, 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).   DOI
15 Gries, D. and Schneider, F. B. (2013). A logical approach to discrete math, Springer Science & Business Media, New York, USA.
16 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.   DOI
17 Tanimoto, T. T. (1958). An elementary mathematical theory of classification and prediction, Internal Report IBM Corp, New York, USA.
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.   DOI