Browse > Article
http://dx.doi.org/10.3807/KJOP.2016.27.5.165

Three-dimensional Geometrical Scanning System Using Two Line Lasers  

Heo, Sang-Hu (Photonics-mediate co., Ltd.)
Lee, Chung Ghiu (Department of Electronic Engineering, Chosun University)
Publication Information
Korean Journal of Optics and Photonics / v.27, no.5, 2016 , pp. 165-173 More about this Journal
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.
Keywords
Laser 3D scanner; 3D reconstruction; Image processing;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 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
2 F. J. Pipitone and T. G. Marshall, "A wide-field scanning triangulation rangefinder for machine vision," Int. J. Rob. Res. 2, 39-49 (1983).   DOI
3 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).
4 F. Blais, "Review of 20 years range sensor development," J. Electron. Imaging 13, 231-243 (2004).   DOI
5 R. Hartley and A. Zisserman, Multiple view geometry in computer vision (Cambridge University Press, Cambridge, UK, 2003).
6 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).
7 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).   DOI
8 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.
9 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.
10 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.
11 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.
12 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).
13 C. Hernandez and F. Schmitt, "Silhouette and stereo fusion for 3D object modeling," Computer Vision and Image Understanding, 96, 397-404 (2004).
14 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).   DOI
15 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).   DOI
16 GML C++ camera calibration toolbox, http://graphics.cs.msu.ru/en/node/909.
17 T. Y. Zhang and C. Y. Suen, "A Fast Parallel Algorithm for Thinning Digital Patterns," Commun. Acm, 27, 236-239 (1984).   DOI
18 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).   DOI