Abstract
The use of 3D reconstruction based on active laser triangulation techniques is very complex in industrial environments. The main problem is that most of these techniques are based on laser stripe extraction methods which are highly sensitive to noise, which is virtually inevitable in these conditions. In industrial environments, variable luminance, reflections which show up in the images as noise, and uneven surfaces are common. These factors modify the shape of the laser profile. This work proposes a fast, accurate, and robust method to extract laser stripes in industrial environments. Specific procedures are proposed to extract the laser stripe projected on the background, using a boundary linking process, and on the foreground, using an improved Split-and-Merge approach with different approximation functions including linear, quadratic, and Akima splines. Also, a novel procedure to automatically define the region of interest in the image is proposed. The real-time performance of the proposed method is analyzed by measuring the time taken by the tasks involved in their application. Finally, the proposed extraction method is applied to two real applications: 3D reconstruction of steel strips and weld seam tracking.
Similar content being viewed by others
References
Faugeras O.: Three-Dimensional Computer Vision: A Geometric Viewpoint. Mit Press, Cambridge (1993)
Frauel Y., Tajahuerce E., Matoba O., Castro A., Javidi B.: Comparison of passive ranging integral imaging and active imaging digital holography for three-dimensional object recognition. Appl. Opt. 43(2), 452–462 (2004)
Kriegman D.J., Triendl E., Binford E.: Stereo vision and navigation in buildings for mobile robots. IEEE Trans. Robot. Autom. 5(6), 792–803 (1989)
Ballan, L., Cortelazzo, G.M.: Multimodal 3D shape recovery from texture, silhouette and shadow information. In: Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT’06), pp 924–930 (2006)
Salvi J., Pagès J., Batlle J.: Pattern codification strategies in structured light systems. Pattern Recognit. 37(4), 827–849 (2004)
Forest, J., Salvi, J.: A review of laser scanning three-dimensional digitisers. In: IEEE/RSJ International Conference on Intelligent Robots and System, vol. 1 (2002)
Forest, J., Salvi, J., Cabruja, E., Pous, C.: Laser stripe peak detector for 3d scanners. A FIR filter approach. In: Pattern Recognition, Proceedings of the 17th International Conference on ICPR 2004, vol. 3 (2004)
Levoy, M., Pulli, K., Curless, B., Rusinkiewicz, S., Koller, D., Pereira, L., Ginzton, M., Anderson, S., Davis, J., Ginsberg, J., et al.: The digital Michelangelo project: 3D scanning of large statues. In: Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques, pp. 131–144 (2000)
Robinson A., Alboul L., Rodrigues M.: Methods for indexing stripes in uncoded structured light scanning systems. J. WSCG 12(3), 371–378 (2004)
Haug K., Pritschow G.: Robust laser-stripe sensor for automated weld-seam-tracking in the shipbuilding industry. IECON Proc. Ind. Electron. Conf. 2, 1236–1241 (1998)
Orghidan R., Salvi J., Mouaddib E.M.: Modelling and accuracy estimation of a new omnidirectional depth computation sensor. Pattern Recognit. Lett. 27(7), 843–853 (2006)
Vodanovic B.: Structured light tracks seams. Sens. Rev. 16(1), 35–39 (1996)
Fisher, R.B., Naidu, D.K.: A comparison of algorithms for subpixel peak detection. In: Image Technology: Advances in Image Processing, Multimedia and Machine Vision (1996)
Gonzalez R.C., Woods R.E.: Digital Image Processing. Addison-Wesley, Reading, MA (1987)
Roberts, L.G.: Machine perception of three-dimensional solids. MIT Lincoln Laboratory Technical Report No 315, 22 May 1963
Illingworth J., Kittler J.: A survey of the Hough transform. Comput. Vis. Gr. Image Process. 44(1), 87–116 (1988)
Duda R.O., Hart P.E.: Use of the Hough transformation to detect lines and curves in pictures. Commun. ACM 15(1), 11–15 (1972)
Yang S., Cho M., Lee H., Cho T.: Weld line detection and process control for welding automation. Meas. Sci. Technol. 18(3), 819–826 (2007)
Duda R.O., Hart P.E. et al.: Pattern Classification and Scene Analysis. Wiley, New York (1973)
Ramer U.: An iterative procedure for the polygonal approximation of plane curves. Comput. Gr. Image Process. 1(3), 244–256 (1972)
Pavlidis T., Horowitz S.L.: Segmentation of plane curves. Trans. Comput. 100(23), 860–870 (1974)
Dunham J.G.: Optimum uniform piecewise linear approximation of planar curves. IEEE Trans. Pattern Anal. Mach. Intell. 8(1), 67–75 (1986)
Ridler T.W., Calvard S.: Picture thresholding using an iterative selection method. IEEE Trans. Syst. Man Cybern. 8(8), 630–632 (1978)
Akima H.: A new method of interpolation and smooth curve fitting based on local procedures. J. ACM 17(4), 589–602 (1970)
Molleda J., Usamentiaga R., Garcia D.F., Bulnes F.: Real-time flatness inspection of rolled products based on optical laser triangulation and three-dimensional surface reconstruction. J. Electron. Imaging 19, 031206 (2010). doi:10.1117/1.3455987
Xiao X., Shi Y., Wang G., Li H.: Study of image processing for v-shape groove and robotic weld seam tracking based on laser vision. China Weld. (English Edition) 17(4), 68–73 (2008)
Fernandez, A., Garcia, R., Alvarez, E., Campos, A., Garcia, D.F., Usamentiaga, R., Jimenez, M., Garcia, J.M.: Low cost system for weld tracking based on artificial vision. In: IEEE Industry Applications Conference, pp. 1–8 (2009)
Heikkila, J., Silven, O.: Four-step camera calibration procedure with implicit image correction. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1102–1112 (1997)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Usamentiaga, R., Molleda, J. & García, D.F. Fast and robust laser stripe extraction for 3D reconstruction in industrial environments. Machine Vision and Applications 23, 179–196 (2012). https://doi.org/10.1007/s00138-010-0288-6
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00138-010-0288-6