Constrained Monocular Obstacle Perception with Just One Frame

  • Lluís Pacheco
  • Xavier Cufí
  • Javi Cobos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4477)


This paper presents a monocular perception system tested on wheeled mobile robots. Its significant contribution is the use of a single image to obtain depth information (one bit) when robots detect obstacles. The constraints refer to the environment. Flat and homogeneous floor radiance is assumed. Results emerge from using a set of multi-resolution focus measurement thresholds to avoid obstacle collision. The algorithm’s simplicity and the robustness achieved can be considered the key points of this work. On-robot experimental results are reported and a broad range of indoor applications is possible. However, false obstacle detection occurs when the constraints fail. Thus, proposals to overcome it are explained.


Mobile Robot Machine Vision System Obstacle Detection Navigation Strategy Focus Measure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Horn, B.K.P.: Robot Vision (12th printing). McGraw-Hill, New York (1998)Google Scholar
  2. 2.
    Schechner, Y., Kiryati, N.: Depth from Defocus vs. Stereo: How Different Really Are They? In: Proceedings Fourteenth Int. Conf. on Pattern Recognition, vol. 2 (1998)Google Scholar
  3. 3.
    Campbell, J., Sukthankar, R., Noubakhsh, L.: Techniques for Evaluating Optical Flow for Visual Odometry in Extreme Terrain. In: Proc. of IROS (2004)Google Scholar
  4. 4.
    Nourbakhsh, I.R., Andre, D., Tomasi, C., Genesereth, M.R.: Mobile Robot Obstacle Avoidance Via Depth From Focus. Robotics and Autonomous Systems 22, 151–158 (1997)CrossRefGoogle Scholar
  5. 5.
    Krotkov, E.: Focusing. In MS-CIS-86-22. Grasp Lab 63. Dept. of Computer and Information Science. University of Pennsylvania (1986)Google Scholar
  6. 6.
    Subbarao, M., Choi, T., Nikzad, A.: Focusing Techniques. Tech. Report 92.09.04, Stony Brook, New York (1992)Google Scholar
  7. 7.
    Nayar, S.K., Nakagawa, Y.: Shape from Focus. IEEE Trans. PAMI 16(8) (1994)Google Scholar
  8. 8.
    Subbarao, M., Tyan, J.K.: Selecting the Optimal Focus Measure for Autofocusing and Depth-from-Focus. IEEE Trans. PAMI 20(8) (1998)Google Scholar
  9. 9.
    Subbarao, M., Tyan, J.K.: Root-Mean Square Error in Passive Autofocusing and 3D Shape Recovery. In: Proc. of SPIE’s International Symposium, on Three- Dimensional Imaging and Laser-Based Systems for Metrology and Inspection II, vol. 2909, pp. 162–177 (1996)Google Scholar
  10. 10.
    Choi, T., Yun, J.: Accurate 3-D Shape Recovery using Curved Window Focus Measure. In: Proc. of ICIP, vol. 3, pp. 910–914 (1999)Google Scholar
  11. 11.
    Pentland, A.P.: A New Sense for Depth of Field. IEEE Trans. Pattern Anal. Machine Intelligence 9, 523–531 (1987)CrossRefGoogle Scholar
  12. 12.
    Subbarao, M., Surya, G.: Depth from Defocus: A Spatial Domain Approach. International Journal of Computer Vision 13(3), 271–294 (1994)CrossRefGoogle Scholar
  13. 13.
    Xiong, Y., Shafer, S.A.: Moment Filters for High Precision Computation of Focus and Stereo. In: IEEE/RSJ Inter. Conf. on Intel. Robots and Systems, August 1995, pp. 108–113 (1995)Google Scholar
  14. 14.
    Rajagopalan, A.N., Chaudhuri, S.: Identification of Shift-Variant Point Spread Function For a Class of Imaging Systems. In: Proc. of IEEE Speech and Image Technologies for Computing and Telecommunications, Dec. 1997, vol. 1, pp. 275–278 (1997)Google Scholar
  15. 15.
    Nayar, S.K., Watanabe, M., Noguchi, M.: Real-Time Focus Range sensor. IEEE Trans. on PAMI 18(12) (1996)Google Scholar
  16. 16.
    Surya, G.: Three Dimensional Scene Recovery from Image Defocus. PHD thesis, Stony Brook, New York (December 1994)Google Scholar
  17. 17.
    Wang, Y., Bahrami, S., Zhu, S.: Perceptual Scale Space and Its Apliccations. In: Proc. of IEEE Conf. on Computer Vision, ICCV05, vol. 1, pp. 58–65 (2005)Google Scholar
  18. 18.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice Hall, Englewood Cliffs (2002)Google Scholar
  19. 19.
    Pacheco, L., Batlle, J., Cufí, X., Arbusé, R.: PRIM an Open Mobile Robot Platform, Present and Future Trends. In: Proc. of IEEE-TTTC, AQTR (2006)Google Scholar
  20. 20.
    Pacheco, L., Ningsu, L., Arbusé, R.: Experimental Modelling and Control Strategies on an Open Robot Platform PRIM. In: Proc. of IEEE-TTTC AQTR (2006)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Lluís Pacheco
    • 1
  • Xavier Cufí
    • 1
  • Javi Cobos
    • 1
  1. 1.Computer Vision and Robotics Group, Institute of Informatics and Applications, University of Girona, Av. Lluís Santaló sn, 17071 GironaSpain

Personalised recommendations