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Three-dimensional Geometrical Scanning System Using Two Line Lasers

2-라인 레이저를 사용한 3차원 형상 복원기술 개발

  • Received : 2016.06.14
  • Accepted : 2016.09.29
  • Published : 2016.10.25

Abstract

In this paper, we propose a three-dimensional (3D) scanning system based on two line lasers. This system uses two line lasers with different wavelengths as light sources. 532-nm and 630-nm line lasers can compensate for missing scan data generated by geometrical occlusion. It also can classify two laser planes by using the red and green channels. For automatic registration of scanning data, we control a stepping motor and divide the motor's rotational degree of freedom into micro-steps. To this end, we design a control printed circuit board for the laser and stepping motor, and use an image processing board. To compute a 3D point cloud, we obtain 200 and 400 images with laser lines and segment lines on the images at different degrees of rotation. The segmented lines are thinned for one-to-one matching of an image pixel with a 3D point.

본 연구는 2개의 라인 레이저를 이용하여 3차원 형상을 획득하는 방법에 대해 제안한다. 제안하는 방법은 532 nm와 630 nm 파장의 레이저를 이용하여 2-라인 레이저를 생성하고 이를 대상객체에 조사하여 반사되는 빛을 영상센서로 획득하는 것을 통해 3차원 점 데이터를 연산한다. 이를 위해 레이저와 카메라 간의 위치를 결정하고 각 레이저의 평면 방정식 계수를 추정하며 삼각법을 통해 이미지 공간의 라인을 3차원 공간의 점으로 변환한다. 제안하는 시스템은 2개의 라인 레이저와 데이터 정합을 위한 스태핑 모터 제어부와 영상을 획득하고 레이저의 라인을 추출하는 영상처리부, 그리고 추출된 라인으로부터 3차원 점 데이터를 처리하고 3D 모델을 생성하는 3D 모델링부로 나뉜다. 제안하는 방법은 기존 단일 레이저 스캐닝 방식과 비교하여 가려짐으로 인해 발생하는 데이터 소실문제를 해결할 수 있다.

Keywords

References

  1. F. J. Pipitone and T. G. Marshall, "A wide-field scanning triangulation rangefinder for machine vision," Int. J. Rob. Res. 2, 39-49 (1983). https://doi.org/10.1177/027836498300200104
  2. E. L. Hall, J. B. K. Tio, C. A. McPherson, and Fl. A. Sadjadi, "Measuring curved surfaces for robot vision," Computer 15, 42-54 (1982).
  3. F. Blais, "Review of 20 years range sensor development," J. Electron. Imaging 13, 231-243 (2004). https://doi.org/10.1117/1.1631921
  4. R. Hartley and A. Zisserman, Multiple view geometry in computer vision (Cambridge University Press, Cambridge, UK, 2003).
  5. M. Himmelsbach, A. Muller, T. Luttel, and H. J. Wunsche, "LIDAR-based 3D object perception," Proceedings of 1st International Workshop on Cognition for Technical Systems (2008).
  6. J. R. Rosell, J. Llorens, R. Sanz, J. Arno, M. Ribes-dasi, J. Masip, A. Escola, F. Camp, F. Solanelles, F. G. cia, E. gil, L. Val, S. Planas, and J. Palacin "Obtaining the three-dimensional structure of tree orchards from remote 2D terrestrial LIDAR scanning," Agr. Forest. Meteorol. 149, 1505-1515 (2009). https://doi.org/10.1016/j.agrformet.2009.04.008
  7. B. Koyuncu and K. Kullu, "Development of an Optical 3D Scanner Based on Structured Light," Proceedings of the 9th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases (Cambridge, UK, Feb. 2010), pp. 17-22.
  8. M. Quigley, S. Batra, S. Gould, and E. Klingbeil "High-Accuracy 3D Sensing for Mobile Manipulation: Improving Object Detection and Door Opening," 2009 IEEE International Conference on Robotics and Automation (Kobe International Conference Center, Japan, May 2009), pp. 2816-2822.
  9. C. Rocchini, P. Cignoni, C. Montani, P. Pingi, and R. Scopigno, "A low cost 3D scanner based on structured light," Eurographics 2001(2001), pp. 209-308.
  10. M. Gupta, A. Agrawal, A. Veeraraghavan, and S. G. Narasimhan, "Structured Light 3D Scanning in the Presence of Global Illumination," Computer Vision and Pattern Recognition (Colorado Springs, Colorado, USA, Jun. 2011), pp. 713-720.
  11. J. P. Pons, R. Keriven, and O. Faugeras, "Multi-view stereo reconstruction and scene flow estimation with a global image-based matching score," Int. J. Comput. Vis., 72(2), pp. 173-193 (2007).
  12. C. Hernandez and F. Schmitt, "Silhouette and stereo fusion for 3D object modeling," Computer Vision and Image Understanding, 96, 397-404 (2004).
  13. S. J. Lee, M. K. Park, I. Y. Jang, and K. H. Lee, "Fast multiview three-dimensional reconstruction method using cost volume filtering," Opt. Eng., 53, 033104 (2014). https://doi.org/10.1117/1.OE.53.3.033104
  14. S. J. Lee, M. K. Park, and K. H. Lee, "Full 3D surface reconstruction of partial scan data with noise and different levels of scale," J. Mech. Sci. Technol., 28, 3171- 3180 (2014). https://doi.org/10.1007/s12206-014-0726-x
  15. GML C++ camera calibration toolbox, http://graphics.cs.msu.ru/en/node/909.
  16. T. Y. Zhang and C. Y. Suen, "A Fast Parallel Algorithm for Thinning Digital Patterns," Commun. Acm, 27, 236-239 (1984). https://doi.org/10.1145/357994.358023
  17. M. K. Park, S. J. Lee, and K. H. Lee, "Multi-scale tensor voting for feature extraction from unstructured point clouds," Graph. Models, 74, 197-208 (2012). https://doi.org/10.1016/j.gmod.2012.04.008
  18. M. Kazhdan, M. Bolitho, and H. Hoppe, "Poisson surface reconstruction," SPG 2006 Proceeding of the fourth Eurographics symposium on Geometry processing, (Sardinia, Italy, Jun. 2006) pp. 61-70