Lattice Independent Component Analysis for Mobile Robot Localization

  • Ivan Villaverde
  • Borja Fernandez-Gauna
  • Ekaitz Zulueta
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6077)


This paper introduces an approach to appearance based mobile robot localization using Lattice Independent Component Analysis (LICA). The Endmember Induction Heuristic Algorithm (EIHA) is used to select a set of Strong Lattice Independent (SLI) vectors, which can be assumed to be Affine Independent, and therefore candidates to be the endmembers of the data. Selected endmembers are used to compute the linear unmixing of the robot’s acquired images. The resulting mixing coefficients are used as feature vectors for view recognition through classification. We show on a sample path experiment that our approach can recognise the localization of the robot and we compare the results with the Independent Component Analysis (ICA).


Feature Vector Independent Component Analysis Independent Component Analysis Robot Localization Landmark Position 
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.
  2. 2.
    Graña, M., Torrealdea, F.: Hierarchically structured systems. European Journal of Operational Research 25, 20–26 (1986)CrossRefGoogle Scholar
  3. 3.
    Graña, M.: A brief review of Lattice Computing. In: IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2008 (IEEE World Congress on Computational Intelligence), June 2008, pp. 1777–1781 (2008)Google Scholar
  4. 4.
    Graña, M., Savio, A.M., García-Sebastián, M., Fernandez, E.: A Lattice Computing approach for on-line fMRI analysis. Image and Vision Computing (in Press Corrected Proof, 2009)Google Scholar
  5. 5.
    Graña, M., Villaverde, I., Maldonado, J.O., Hernandez, C.: Two Lattice Computing approaches for the unsupervised segmentation of hyperspectral images. Neurocomputing 72(10-12), 2111–2120 (2009)CrossRefGoogle Scholar
  6. 6.
    Hyvärinen, A., Karhunen, J., Oja, E.: Independent component analysis. John Wiley and Sons, Chichester (2001)CrossRefGoogle Scholar
  7. 7.
    Højen-Sørensen, P., Winther, O., Hansen, L.K.: Mean-field approaches to independent component analysis. Neural Computation 14(4), 889–918 (2002)CrossRefGoogle Scholar
  8. 8.
    Jones, S., Andresen, C., Crowley, J.: Appearance based process for visual navigation. In: Proceedings of the 1997 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 1997, September 1997, vol. 2, pp. 551–557 (1997)Google Scholar
  9. 9.
    Keshava, N., Mustard, J.: Spectral unmixing. IEEE Signal Processing Magazine 19(1), 44–57 (2002)CrossRefGoogle Scholar
  10. 10.
    Kröse, B., Vlassis, N., Bunschoten, R.: Omnidirectional vision for Appearance-Based robot localization. In: Hager, G.D., Christensen, H.I., Bunke, H., Klein, R. (eds.) Dagstuhl Seminar 2000. LNCS, vol. 2238, pp. 39–50. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  11. 11.
    Molgedey, L., Schuster, H.G.: Separation of a mixture of independent signals using time delayed correlations. Physical Review Letters 72, 3634–3637 (1994)CrossRefGoogle Scholar
  12. 12.
    Munguia, R., Grau, A., Sanfeliu, A.: Matching images features in a wide base line with ICA descriptors. In: 18th International Conference on Pattern Recognition, ICPR 2006, vol. 2, pp. 159–162 (2006)Google Scholar
  13. 13.
    Ritter, G.X., Gader, P.: Fixed points of Lattice Transforms and Lattice Associative Memories. In: Advances in Imaging and Electron Physics, vol. 144, pp. 165–242. Elsevier, Amsterdam (2006)Google Scholar
  14. 14.
    Ritter, G.X., Urcid, G., Schmalz, M.: Autonomous single-pass endmember approximation using Lattice Auto-Associative Memories. Neurocomputing 72(10-12), 2101–2110 (2009)CrossRefGoogle Scholar
  15. 15.
    Sim, R., Dudek, G.: Learning generative models of scene features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001, vol. 1, pp. 406–412 (2001)Google Scholar
  16. 16.
    Sussner, P., Valle, M.: Gray-scale Morphological Associative Memories. IEEE Transactions on Neural Networks 17(3), 559–570 (2006)CrossRefGoogle Scholar
  17. 17.
    Ulrich, I., Nourbakhsh, I.: Appearance-based place recognition for topological localization. In: Proceedings of IEEE International Conference on Robotics and Automation, ICRA 2000, vol. 2, pp. 1023–1029 (2000)Google Scholar
  18. 18.
    Urcid, G., Valdiviezo, J.C.: Generation of lattice independent vector sets for pattern recognition applications. In: Ritter, G.X., Schmalz, M.S., Barrera, J., Astola, J.T. (eds.) Proc. of SPIE 2007, Math. of Data/Image Pattern Recog. Compression, Coding and Encrip. with Applications X, vol. 6700, pp. 67000C:1–12. SPIE, San Jose (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Ivan Villaverde
    • 1
  • Borja Fernandez-Gauna
    • 1
  • Ekaitz Zulueta
    • 1
  1. 1.Computational Intelligence Group, Dept. CCIAUPV/EHUSan SebastianSpain

Personalised recommendations