Skip to main content
Log in

Fast and robust laser stripe extraction for 3D reconstruction in industrial environments

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Faugeras O.: Three-Dimensional Computer Vision: A Geometric Viewpoint. Mit Press, Cambridge (1993)

    Google Scholar 

  2. 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)

    Article  Google Scholar 

  3. 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)

    Article  Google Scholar 

  4. 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)

  5. Salvi J., Pagès J., Batlle J.: Pattern codification strategies in structured light systems. Pattern Recognit. 37(4), 827–849 (2004)

    Article  MATH  Google Scholar 

  6. 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)

  7. 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)

  8. 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)

  9. Robinson A., Alboul L., Rodrigues M.: Methods for indexing stripes in uncoded structured light scanning systems. J. WSCG 12(3), 371–378 (2004)

    Google Scholar 

  10. 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)

    Google Scholar 

  11. 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)

    Article  Google Scholar 

  12. Vodanovic B.: Structured light tracks seams. Sens. Rev. 16(1), 35–39 (1996)

    Article  Google Scholar 

  13. 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)

  14. Gonzalez R.C., Woods R.E.: Digital Image Processing. Addison-Wesley, Reading, MA (1987)

    Google Scholar 

  15. Roberts, L.G.: Machine perception of three-dimensional solids. MIT Lincoln Laboratory Technical Report No 315, 22 May 1963

  16. Illingworth J., Kittler J.: A survey of the Hough transform. Comput. Vis. Gr. Image Process. 44(1), 87–116 (1988)

    Article  Google Scholar 

  17. 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)

    Article  Google Scholar 

  18. 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)

    Article  Google Scholar 

  19. Duda R.O., Hart P.E. et al.: Pattern Classification and Scene Analysis. Wiley, New York (1973)

    MATH  Google Scholar 

  20. Ramer U.: An iterative procedure for the polygonal approximation of plane curves. Comput. Gr. Image Process. 1(3), 244–256 (1972)

    Article  Google Scholar 

  21. Pavlidis T., Horowitz S.L.: Segmentation of plane curves. Trans. Comput. 100(23), 860–870 (1974)

    Article  MathSciNet  Google Scholar 

  22. Dunham J.G.: Optimum uniform piecewise linear approximation of planar curves. IEEE Trans. Pattern Anal. Mach. Intell. 8(1), 67–75 (1986)

    Article  Google Scholar 

  23. Ridler T.W., Calvard S.: Picture thresholding using an iterative selection method. IEEE Trans. Syst. Man Cybern. 8(8), 630–632 (1978)

    Article  Google Scholar 

  24. Akima H.: A new method of interpolation and smooth curve fitting based on local procedures. J. ACM 17(4), 589–602 (1970)

    Article  MATH  Google Scholar 

  25. 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

    Article  Google Scholar 

  26. 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)

    Google Scholar 

  27. 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)

  28. 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)

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rubén Usamentiaga.

Rights and permissions

Reprints 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

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-010-0288-6

Keywords

Navigation