Advertisement

Extraction of Haar Integral Features on Omnidirectional Images: Application to Local and Global Localization

  • Ouiddad Labbani-Igbida
  • Cyril Charron
  • El Mustapha Mouaddib
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4174)

Abstract

In this paper, we present a new method for producing omnidirectional image signatures that are purposed to localize a mobile robot in an office environment. To solve the problem of perceptual aliasing common to the image based recognition approaches, we choose to build signatures that greatly vary between rooms and slowly vary inside a given room. We suggest an averaging technique based on Haar integral invariance. It takes into account the movements the robot can do in a room and the omni image transformations thus produced.

The variability of the built signatures is adjusted (total or partial Haar invariance) according to defined subsets of the group transformation. The experimental results prove to get significantly interesting results for place recognition and robot localization with variable accuracy: From global rough localization to local precise one.

Keywords

Mobile Robot Image Retrieval Scale Invariant Feature Transform Robot Localization Image Retrieval System 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Castellanos, J.A., Tardos, J.D.: Mobile robot localization and map building: A multisensor fusion approach. Kluwer Academic Publishers, Boston (2000)Google Scholar
  2. 2.
    Borenstein, J., Everett, B., Feng, L.: Navigating mobile robots: Systems and techniques. A. K. Peters, Ltd., Wellesley (1996)MATHGoogle Scholar
  3. 3.
    Ishiguro, H., Tsuji, S.: Image-based memory of environment. In: IEEE/RSJ International Conference on Intelligent RObots and Systems, vol. 2, pp. 634–639 (1996)Google Scholar
  4. 4.
    Ulrich, I., Nourbakhsh, I.: Appearance-based place recognition for topological localization. In: International Conference on Robotics and Automation, pp. 1023–1029. IEEE, San Francisco (2000)Google Scholar
  5. 5.
    Gonzalez, J., Lacroix, S.: Rover localization in natural environments by indexing panoramic images. In: IEEE International Conference on Robotics and Automation, pp. 1365–1370 (2002)Google Scholar
  6. 6.
    Ishikawa, M., Kawashima, S., Homma, N.: Memory-based location estimation and navigation using bayesian estimation. In: International Conference on Neural Information Processing, vol. 1, pp. 112–117 (October 1998)Google Scholar
  7. 7.
    Menegatti, E., Maeda, T., Ishiguro, H.: Image-based memory for robot navigation using properties of the omnidirectional images. Robotics and Autonomous Systems 47(4), 251–267 (2004)CrossRefGoogle Scholar
  8. 8.
    Menegatti, E., Zoccarato, M., Pagello, E., Ishiguro, H.: Image-based monte-carlo localisation with omnidirectional images. Robotics and Autonomous Systems 48(1), 17–30 (2004)CrossRefGoogle Scholar
  9. 9.
    Pajdla, T., Hlaváč, V.: Zero phase representation of panoramic images for image based localization. In: Solina, F., Leonardis, A. (eds.) CAIP 1999. LNCS, vol. 1689, pp. 550–557. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  10. 10.
    Jogan, M., Leonardis, A.: Robust localization using an omnidirectional appearance-based subspace model of environment. Robotics and Autonomous Systems 45(1) (2003)Google Scholar
  11. 11.
    Maeda, S., Kuno, Y., Shirai, Y.: Active navigation vision based on eigenspace analysis. In: International Conference on Intelligent Robots and Systems, pp. 1018–1023. IEEE/RSJ (1997)Google Scholar
  12. 12.
    Leonardis, A., Bischof, H.: Robust recognition using eigenimages. Computer Vision and Image Understanding Special Issue on Robust Statistical Techniques in Image Understanding 78(1), 99–118 (2000)Google Scholar
  13. 13.
    Gaspar, J., Winters, N., Santos-Victor, J.: Vision-based navigation and environmental representations with an omnidirectional camera. IEEE Transactions on Robotics and Automation 16(6), 890–898 (2000)CrossRefGoogle Scholar
  14. 14.
    Lowe, D.: Object recognition from local scale-invariant features. In: International Conference on Computer Vision, pp. 1150–1157 (September 1999)Google Scholar
  15. 15.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)Google Scholar
  16. 16.
    Andreasson, H., Treptow, A., Duckett, T.: Localization for mobile robots using panoramic vision, local features and particle filter. In: IEEE International Conference on Robotics and Automation (2005)Google Scholar
  17. 17.
    Tamimi, H., Andreasson, H., Treptow, A., Duckett, T., Zell, A.: Localization of mobile robots with omnidirectional vision using particle filter and iterative sift. In: European Conference on Mobile Robots, Ancona, Italy (2005)Google Scholar
  18. 18.
    Schulz-Mirbach, H., Burkhardt, H., Sigglekow, S.: Using invariant features for content based data retrieval. In: Workshop on Nonlinear Methods in Model-Based Image Interpretation, Lausanne, Switzerland, pp. 1–5 (September 1996)Google Scholar
  19. 19.
    Sigglekow, S.: Feature histograms for content-based image retrieval. Ph.D. dissertation, Universitat Freiburg im Breusgau (2002)Google Scholar
  20. 20.
    Halawani, A., Burkhardt, H.: Image retrieval by local evaluation of nonlinear kernel functions around salient points. In: International Conference on Pattern Recognition, vol. 2, pp. 955–960 (August 2004)Google Scholar
  21. 21.
    Halawani, A., Burkhardt, H.: On using histograms of local invariant features for image retrieval. In: IAPR Workshop on Machine Vision Applications, pp. 538–541 (May 2005)Google Scholar
  22. 22.
    Wolf, H.B.J., Burgard, W.: Using an image retrieval system for vision-based mobile robot localization. In: Lew, M.S., Sebe, N., Eakins, J.P. (eds.) Proc. of the International Conference on Image and Video Retrieval (CIVR), pp. 108–119. Springer, Heidelberg (2002)Google Scholar
  23. 23.
    Geyer, C., Daniilidis, K.: Catadioptric projective geometry. International Journal of Computer Vision 45(3), 223–243 (2001)MATHCrossRefGoogle Scholar
  24. 24.
    Lindeberg, T.: Scale-space theory: A basic tool for analysing structures at different scales. Journal of Applied Statistics 21(2), 224–270 (1994)Google Scholar
  25. 25.
    Charron, C., Labbani-Igbida, O., Mouaddib, E.: On Building Omnidirectional Image Signatures Using Haar Invariant Features: Application to the Localization of Robots. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2006. LNCS, vol. 4179, pp. 1099–1110. Springer, Heidelberg (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ouiddad Labbani-Igbida
    • 1
  • Cyril Charron
    • 1
  • El Mustapha Mouaddib
    • 1
  1. 1.Centre de Robotique, Electrotechnique et Automatique, Université de Picardie Jules VerneAmiensFrance

Personalised recommendations