Journal of Computational Design and Engineering

Society for Computational Design and Engineering (한국CDE학회)
권호 표지
DOI QR코드

DOI QR Code

Efficient point cloud data processing in shipbuilding: Reformative component extraction method and registration method

  • Sun, Jingyu (Graduate School of Frontier Sciences, University of Tokyo) ;
  • Hiekata, Kazuo (Graduate School of Frontier Sciences, University of Tokyo) ;
  • Yamato, Hiroyuki (Graduate School of Frontier Sciences, University of Tokyo) ;
  • Nakagaki, Norito (Sumitomo Heavy Industries Marine & Engineering Co., Ltd.) ;
  • Sugawara, Akiyoshi (Sumitomo Heavy Industries Marine & Engineering Co., Ltd.)
  • Received : 2014.02.27
  • Accepted : 2014.05.12
  • Published : 2014.07.01
Download PDF
⟨ Previous Next ⟩

Abstract

To survive in the current shipbuilding industry, it is of vital importance for shipyards to have the ship components' accuracy evaluated efficiently during most of the manufacturing steps. Evaluating components' accuracy by comparing each component's point cloud data scanned by laser scanners and the ship's design data formatted in CAD cannot be processed efficiently when (1) extract components from point cloud data include irregular obstacles endogenously, or when (2) registration of the two data sets have no clear direction setting. This paper presents reformative point cloud data processing methods to solve these problems. K-d tree construction of the point cloud data fastens a neighbor searching of each point. Region growing method performed on the neighbor points of the seed point extracts the continuous part of the component, while curved surface fitting and B-spline curved line fitting at the edge of the continuous part recognize the neighbor domains of the same component divided by obstacles' shadows. The ICP (Iterative Closest Point) algorithm conducts a registration of the two sets of data after the proper registration's direction is decided by principal component analysis. By experiments conducted at the shipyard, 200 curved shell plates are extracted from the scanned point cloud data, and registrations are conducted between them and the designed CAD data using the proposed methods for an accuracy evaluation. Results show that the methods proposed in this paper support the accuracy evaluation targeted point cloud data processing efficiently in practice.

Keywords

References

  1. Biskup K, Arias P, Lorenzo H, Armesto J. Application of trerrestrial laser scanning for shipbuilding. In: Proceedings of ISPRS Workshop on Laser Scanning 2007 and SilviLaser 2007; 2007 Sep 12-14; Espoo, Finland; p. 56-61.
  2. Nakagaki N, Sugawara A, Hiekata K, Yamato H, Sun JY. Development of accuracy evaluation system for curved shell plates using laser scanners (2nd report). Journal of the Japan Society of Naval Architects and Ocean Engineers. 2013; 17: 169-176. https://doi.org/10.2534/jjasnaoe.17.169
  3. Hiekata K, Yamato H, Oida Y, Enomoto M, Furukawa Y. Development and case studies of accuracy evaluation system for curved shell plates by laser scanner. Journal of Ship Production and Design. 2011; 27(2): 84-90.
  4. Pauly M, Gross M, Kobbelt L. Efficient simplification of pointsampled surfaces. In: Proceedings of the Conference on IEEE Visualization; 2002 Oct 27-Nov 1; Boston, MA; p. 163-170.
  5. Forsyth D, Ponce J. Computer vision: a modern approach.. Upper Saddle River (NJ): Prentice Hall Professional Technical Reference; 2007.
  6. Gross M, Pfister H. Point-based graphics. Burlington (MA): Morgan Kaufmann; 2007.
  7. Weingarten JW, Gruener G, Siegwart R. Probabilistic plane fitting in 3D and an application to robotic mapping. In: Proceedings IEEE International Conference on Robotics and Automation (ICRA); 2004 Apr 26- May 1; Zurich, Switzerland; p. 927-932.
  8. Zhang X, Li H, Cheng Z. Curvature estimation of 3D point cloud surfaces through the fitting of normal section curvatures. In: Proceedings of AsiaGraph 2008; 2008 Oct 23-26; Tokyo, Japan; p. 72-79.
  9. Kalogerakis E, Nowrouzezahrai D, Simari P, Singh K. Extracting lines of curvature from noisy point clouds. Computer-Aided Design. 2009; 41(4): 282-292. https://doi.org/10.1016/j.cad.2008.12.004
  10. Piegl L, Tiller W. The NURBS book. 2nd ed. New York: Springer; 1997.
  11. Okuda H, Kitaaki Y, Hashimoto M, Kaneko S. Fast and highprecision 3-D registration algorithm using hierarchical M-ICP. The Institute of Electronics, Information and Communication Engineers, Technical Report of IEICE. 2004 Sep 10; 8 p. Report No. 2004-CVIM-145.
  12. Besl PJ, McKay ND. Method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1992; 14(2): 239-256. https://doi.org/10.1109/34.121791
  13. Cristobal G, Schelkens P, Thienpont H. Optical and digital image processing. Hoboken (NJ): John Wiley & Sons; 2011.
  14. Zhang Z. Iterative point matching for registration of free-form curves and surfaces. International Journal of Computer Vision. 1994; 13(2): 119-152. https://doi.org/10.1007/BF01427149
  15. FARO [Internet]. FARO Laser Scanner Focus 3D; c2011 [cited 2014 Apr 22] Available from: http://www.globalspec.com/datasheets/579/FARO/F7DD1C91-CE09-4A0A-AB9C-71EC273AC1FA

Cited by

  1. A Novel Real-Time Reference Key Frame Scan Matching Method vol.17, pp.5, 2017, https://doi.org/10.3390/s17051060