Top > IJAT > Line-Based Planar Structure Extraction from a Point Cloud with ...

single-au.php

IJAT Vol.11 No.4 pp. 657-665
doi: 10.20965/ijat.2017.p0657
(2017)

Paper:

Views over last 60 days: 953

Line-Based Planar Structure Extraction from a Point Cloud with an Anisotropic Distribution

Ryuji Miyazaki*,†, Makoto Yamamoto**, and Koichi Harada***

*Faculty of Psychological Science, Hiroshima International University
555-36 Gakuendai, Kurose, Higashi-hiroshima, Hiroshima 739-2695, Japan

†Corresponding author

**Sanei Corporation, Hiroshima, Japan

***Graduate School of Engineering, Hiroshima University, Hiroshima, Japan

Received:
September 17, 2016
Accepted:
April 28, 2017
Online released:
June 29, 2017
Published:
July 5, 2017
Creative Commons License
Keywords:
MMS, point cloud, planar structure, boundary, region growing
Abstract
We propose a line-based region growing method for extracting planar regions with precise boundaries from a point cloud with an anisotropic distribution. Planar structure extraction from point clouds is an important process in many applications, such as maintenance of infrastructure components including roads and curbstones, because most artificial structures consist of planar surfaces. A mobile mapping system (MMS) is able to obtain a large number of points while traveling at a standard speed. However, if a high-end laser scanning system is equipped, the point cloud has an anisotropic distribution. In traditional point-based methods, this causes problems when calculating geometric information using neighboring points. In the proposed method, the precise boundary of a planar structure is maintained by appropriately creating line segments from an input point cloud. Furthermore, a normal vector at a line segment is precisely estimated for the region growing process. An experiment using the point cloud from an MMS simulation indicates that the proposed method extracts planar regions accurately. Additionally, we apply the proposed method to several real point clouds and evaluate its effectiveness via visual inspection.
Cite this article as:
R. Miyazaki, M. Yamamoto, and K. Harada, “Line-Based Planar Structure Extraction from a Point Cloud with an Anisotropic Distribution,” Int. J. Automation Technol., Vol.11 No.4, pp. 657-665, 2017.
Data files:
References
  1. [1] K. Ishikawa, Y. Amano, T. Hashizume, J. Takiguchi, and N. Kajiwara, “A Mobile Mapping System for Precise Road Line Localization Using a Single Camera and 3D Road Model,” J. Robot. Mechatron., Vol.19, No.2, pp. 174-180, 2007.
  2. [2] K. Aoki, K. Yamamoto, and H. Shimamura, “Evaluation Model for Pavement Surface Distress on 3d Point Clouds from Mobile Mapping System,” ISPRS – Int. Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp. 87-90, Jul. 2012.
  3. [3] J. Demantke, B. Vallet, and N. Paparoditis, “Streamed Vertical Rectangle Detection in Terrestrial Laser Scans for Facade Data Production,” ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol.I-3, pp. 99-104, 2012.
  4. [4] J. Demantke, B. Vallet, and N. Paparoditis, “Facade Reconstruction with Generalized 2.5d Grids,” ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol.2, pp. 67-72, Oct. 2013.
  5. [5] K. Kato, H. Seki, and M. Hikizu, “3-D Obstacle Detection Using Laser Range Finder with Polygonal Mirror for Powered Wheelchair,” Int. J. Automation Technol., Vol.9, No.4, pp. 373-380, 2015.
  6. [6] A. Jaakkola, J. Hyyppa, H. Hyyppa, and A. Kukko, “Retrieval Algorithms for Road Surface Modelling Using Laser Based Mobile Mapping,” Sensors, Vol.8, pp. 5238-5249, 2008.
  7. [7] C. Mertz, “Continuous Road Damage Detection Using Regular Service Vehicles,” Proc. of the ITS World Congress, Oct. 2011.
  8. [8] F. Pauling, M. Bosse, and R. Zlot, “Automatic Segmentation of 3D Laser Point Clouds by Ellipsoidal Region Growing,” Proc. of Australasian Conf. on Robotics and Automation, pp. 11-20, dec 2009.
  9. [9] T. Watanabe, T. Niwa, and H. Masuda, “Registration of Point-Clouds from Terrestrial and Portable Laser Scanners,” Int. J. Automation Technol., Vol.10, No.2, pp. 163-171, 2016.
  10. [10] Z. Jakovljevic, R. Puzovic, and M. Pajic, “Recognition of Planar Segments in Point Cloud based on Wavelet Transform,” IEEE Trans. on Industrial Informatics, Vol.11, No.2, pp. 342-352, 2015.
  11. [11] R. Farid and C. Sammut, “Plane-based object categorisation using relational learning,” Machine Learning, Vol.94, No.1, pp. 3-23, 2014.
  12. [12] W. S. Grant, R. C. Voorhies, and L. Itti, “Finding Planes in LiDAR Point Clouds for Real-Time Registration,” Proc. of Int. Conf. on Intelligent Robots and Systems, pp. 4347-4354, IEEE, Nov. 2013.
  13. [13] R. Schnabel, R. Wahl, and R. Klein, “Efficient RANSAC for Point-Cloud Shape Detection,” Computer Graphics Forum, Vol.26, No.2, pp. 214-226, 2007.
  14. [14] J. Xiao, J. Zhang, B. Adler, H. Zhang, and J. Zhang, “Threedimensional Point Cloud Plane Segmentation in Both Structured and Unstructured Environments,” Robotics and Autonomous Systems, Vol.61, No.12, pp. 1641-1652, 2013.
  15. [15] D. Belton and K.-H. Bae, “Automating Post-Processing of Terrestrial Laser Scanning Point Clouds for Road Feature Surveys,” Proc. of the ISPRS Commission V Mid-Term Symp. on Close Range Image Measurement Techniques, Vol.XXXVIII, pp. 74-79, 2010.
  16. [16] R. Farid, “Region-Growing Planar Segmentation for Robot Action Planning,” Proc. of Australasian Joint Conf. on Artificial Intelligence 2015, pp. 179-191, Dec 2015.
  17. [17] H. L. Nguyen, D. Belton, and P. Helmholz, “Scan Profiles Based Method for Segmentation and Extraction of Planar Objects in Mobile Laser Scanning Point Clouds,” ISPRS Int. Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol.XLI-B3, pp. 351-358, 2016.
  18. [18] C. Dyken, M. Daehlen, and T. Sevaldrud, “Simultaneous Curve Simplification,” J. of Geographical Systems, Vol.11, No.3, pp. 273-289, 2009.
  19. [19] D. H. Douglas and T. K. Peucker, “Algorithms for the Reduction of the Number of Points Required to Represent a Digitized Line or its Caricature,” The Canadian Cartographer, Vol.10, No.2, pp. 112-122, 1973.
  20. [20] Z. Zhang and O. D. Faugeras, “Finding clusters and planes from 3D line segments with application to 3D motion determination,” Computer Vision – ECCV’92, pp. 227-236, 1992.
  21. [21] Y. He, C. Zhang, and C. Fraser, “A Line-Based Spectral Clustering Method for Efficient Planar Structure Extraction from Lidar Data,” In ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol.II-5/W2, pp. 103-108, 2013.
  22. [22] R. Miyazaki, M. Yamamoto, E. Hanamoto, H. Izumi, and K. Harada, “A Line-Based Approach for Precise Extraction of Road and Curb Region from Mobile Mapping Data,” ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol.II-5, pp. 243-250, 2014.
  23. [23] M. d. Berg, M. v. Kreveld, M. Overmars, and O. Schwarzkopf, “Computational Geometry: Algorithms and Applications,” pp. 231-237, Springer-Verlag, 1997.
  24. [24] B. Vallet, M. Brédif, A. Serna, B. Marcotegui, and N. Paparoditis, “TerraMobilita/iQmulus urban point cloud analysis benchmark,” Computers and Graphics, Vol.49, pp. 126-133, 2015.
  25. [25] M. Gschwandtner, R. Kwitt, A. Uhl, and W. Pree, “BlenSor: Blender Sensor Simulation Toolbox,” Advances in Visual Computing, pp. 199-208, Springer Berlin Heidelberg, 2011.

Creative Commons License  This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Dec. 02, 2024