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Lattice independent component analysis for appearance-based mobile robot localization

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Abstract

This paper introduces an approach to appearance-based mobile robot localization using a new approach to dimensional reduction based on the notion of Lattice Independence called Lattice Independent Component Analysis (LICA). Any algorithm that can select a set of Strong Lattice Independent (SLI) vectors from the data can be applied inside LICA, this paper applies a specific Endmember Induction Algorithm (EIA) developed by our research group. The fact that SLI vectors are Affine Independent allows the coupling of non-linear Lattice Associative Memories (LAM) and linear unmixing for data exploration and dimensionality reduction. To perform an appearance-based mobile robot visual localization, images from the on-board camera robot are transformed into low dimension feature vector representations for classification. For validation, we compare LICA against several Independent Component Analysis (ICA) approaches over a collection of recorded image sequences taken from the robot following some predefined paths. Results show that LICA improves most of the ICA approaches, and it is only slightly improved by the Molgedey and Schouster ICA in some data instances.

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Notes

  1. http://www.ehu.es/ccwintco/index.php/Pioneer.

  2. http://www.mobilerobots.com/.

References

  1. Fastica site http://www.cis.hut.fi/projects/ica/fastica/

  2. Ica:dtu site http://isp.imm.dtu.dk/toolbox/ica/index.html

  3. Althaus P, Ishiguro H, Kanda T, Miyashita T, Christensen HI (2004) Navigation for human-robot interaction tasks. In: IEEE Press, editor, Proceedings IEEE international conference on robotics and automation, vol 2, pp 1894–1900

  4. Cassinis R, Duina D, Inelli S, Rizzi A (2002) Unsupervised matching of visual landmarks for robotic homing using Fourier-Mellin transform. Rob Auton Syst 40(2–3):131–138

    Article  Google Scholar 

  5. Chatila R (1995) Deliberation and reactivity in autonomous mobile robots. Rob Auton Syst 16(2–4):197–211 Moving the Frontiers between Robotics and Biology

    Article  Google Scholar 

  6. Cummins M, Newman P (2007) Probabilistic appearance based navigation and loop closing. In: Robotics and automation, 2007 IEEE international conference on, pp 2042–2048

  7. Dissanayake G (2001) A solution to the simultaneous localization and map building (SLAM) problem. Rob Autom IEEE Trans 17(3):229–241

    Article  Google Scholar 

  8. Duro R, Graña M, de Lope J (2010) On the potential contributions of hybrid intelligent approaches to Multicomponent Robotic System development. Inf Sci 180(14):2635–2648

    Article  Google Scholar 

  9. Friston KJ, Holmes AP, Worsley KJ, Poline J-B, Frith CD, Frackowiak RSJ (1995) Statistical parametric maps in functional imaging: A general linear approach. Hum Brain Mapp 2:189–210

    Article  Google Scholar 

  10. Graña M (2008) A brief review of lattice computing. In: Fuzzy systems, 2008. FUZZ-IEEE 2008 (IEEE world congress on computational intelligence). In: IEEE international conference on, pp 1777–1781

  11. Graña M, Gallego J (2003) Associative morphological memories for endmember induction. In: Geoscience and remote sensing symposium, 2003. IGARSS ’03. Proceedings. 2003 IEEE International, vol 6, pp 3757–3759

  12. Graña M, Manhaes-Savio A, Garcia-Sebastian M, Fernandez E (2010) A Lattice computing approach for on-line fMRI analysis. Image Vis Comput 28(7):1155–1161 Online pattern recognition and machine learning techniques for computer-vision: Theory and applications

    Article  Google Scholar 

  13. Graña M, Sussner P, Ritter GX (2003) Associative morphological memories for endmember determination in spectral unmixing. In: Fuzzy systems, 2003. FUZZ ’03. The 12th IEEE international conference on, vol 2, pp 1285–1290

  14. Graña M, Villaverde I, Maldonado JO, Hernandez C (2009) Two Lattice computing approaches for the unsupervised segmentation of hyperspectral images. Neurocomputing 72(10-12):2111–2120

    Article  Google Scholar 

  15. Graña M, Chyzhyk D, García-Sebastián M, Hernández C (2011) Lattice independent component analysis for functional magnetic resonance imaging. Inf Sci 181:1910–1928

    Article  Google Scholar 

  16. Graña M, Torrealdea FJ (1986) Hierarchically structured systems. Eur J Oper Res 25:20–26

    Article  Google Scholar 

  17. Hansen LK, Larsen J, Kolenda T (2001) Blind detection of independent dynamic components. In: Acoustics, speech, and signal processing, IEEE international conference on, vol 5, IEEE Computer Society, Los Alamitos, CA, USA, pp 3197–3200

  18. Hojen-Sorensen PAdFR, Winther O, Hansen LK (2002) Mean-field approaches to independent component analysis. Neural Comput 14(4):889–918

    Article  Google Scholar 

  19. Hyvärinen A, Karhunen J, Oja E (2001) Independent component analysis. Wiley, New Jersey

    Book  Google Scholar 

  20. Hyvärinen A, Oja E (1997) A fast fixed-point algorithm for Independent Component Analysis. Neural Comput 9(7):1483–1492

    Article  Google Scholar 

  21. Jones SD, Andresen C, Crowley JL (1997) Appearance based process for visual navigation. In: Intelligent robots and systems, 1997. IROS ’97, Proceedings of the 1997 IEEE/RSJ international conference on, vol 2, pp 551–557

  22. Kaburlasos VG, Pachidis T (2011) A lattice-computing ensemble for reasoning based on formal fusion of disparate data types, and an industrial dispensing application. Inf Fusion (in press)

  23. Kaburlasos VG, Papadakis SE, Amanatiadis A (2011) Binary image 2d shape learning and recognition based on lattice computing (lc) techniques. J Math Imaging Vis (in press)

  24. Keshava N (2003) A survey of spectral unmixing algorithms. Lincoln Laboratory Journal 14(1):55–78

    Google Scholar 

  25. Keshava N, Mustard JF (2002) Spectral unmixing. Signal Process Mag IEEE 19(1):44–57

    Article  Google Scholar 

  26. Krose B, Vlassis N, Bunschoten R (2002) Omnidirectional vision for appearance-based robot localization. In: Sensor based intelligent robots, vol 2238/2002 of lecture notes in computer science, pp 39–50

  27. Krose BJA, Vlassis N, Bunschoten R, Motomura Y (2001) A probabilistic model for appearance-based robot localization. Image Vis Comput 6(19):381–391

    Article  Google Scholar 

  28. Lawson CL, Hanson RJ (1974) Solving least squares problems. Prentice-Hall, Englewood Cliffs

    MATH  Google Scholar 

  29. Mardia KV, Kent JT, Bibby JM (1980) Multivariate analysis. Academic Press, Massachusetts

    Google Scholar 

  30. Molgedey L, Schuster HG (1994) Separation of a mixture of independent signals using time delayed correlations. Phys Rev Lett 72:3634–3637

    Article  Google Scholar 

  31. Munguia R, Grau A, Sanfeliu A (2006) Matching images features in a wide base line with ICA descriptors. In: Pattern recognition, 2006. ICPR 2006. 18th international conference on, vol 2, pp 159–162

  32. Newman P, Cole D, Ho K (2006) Outdoor SLAM using visual appearance and laser ranging. In: Robotics and automation, 2006. ICRA 2006. Proceedings 2006 IEEE international conference on, pp 1180–1187

  33. Papadakis SE, Kaburlasos VG (2010) Piecewise-linear approximation of non-linear models based on probabilistically/possibilistically interpreted intervals’ numbers (ins). Inf Sci 180(24):5060–5076

    Article  MATH  Google Scholar 

  34. Raducanu B, Graña M, Albizuri FX (2003) Morphological scale spaces and associative morphological memories: results on robustness and practical applications. J Math Imaging Vis 19(2):113–131

    Article  MATH  Google Scholar 

  35. Ritter GX, Diazde Leon JL, Sussner P (1999) Morphological bidirectional associative memories. Neural Netw 12(6):851–867

    Article  Google Scholar 

  36. Ritter GX, Urcid G, Iancu L (2003) Reconstruction of patterns from noisy inputs using morphological associative memories. J Math Imaging Vis 19(2):95–111

    Article  MathSciNet  MATH  Google Scholar 

  37. Ritter GX, Gader P (2006) Fixed points of Lattice transforms and Lattice associative memories, vol 144 of advances in imaging and electron physics. Elsevier, pp 165–242

  38. Ritter GX, Sussner P, Diazde Leon JL (1998) Morphological associative memories. Neural Netw IEEE Trans 9(2):281–293

    Article  Google Scholar 

  39. Ritter GX, Urcid G (2011) A lattice matrix method for hyperspectral image unmixing. Inf Sci 181:1787–1803

    Article  MathSciNet  MATH  Google Scholar 

  40. Ritter GX, Urcid G, Schmalz MS (2009) Autonomous single-pass endmember approximation using Lattice auto-associative memories. Neurocomputing 72(10–12):2101–2110

    Article  Google Scholar 

  41. Se S, Lowe D, Little J (2002) Mobile robot localization and mapping with uncertainty using scale-invariant visual landmarks. Int J Robot Res 21(8):735–758

    Article  Google Scholar 

  42. Sim R, Dudek G (2001) Learning generative models of scene features. In: Computer vision and pattern recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE computer society conference on, vol 1, pp 406–412

  43. Thrun S (1998) Learning metric-topological maps for indoor mobile robot navigation. Artif Intell 99(1):21–71

    Article  MATH  Google Scholar 

  44. Thrun S, Bennewitz M, Burgard W, Cremers AB, Dellaert F, Fox D, Hahnel D, Rosenberg C, Roy N, Schulte J, Schulz D (1999) Minerva: a second-generation museum tour-guide robot. In: Robotics and automation, 1999. Proceedings. 1999 IEEE international conference on, vol 3, pp 1999–2005

  45. Ulrich I, Nourbakhsh I (2000) Appearance-based place recognition for topological localization. In: Robotics and automation, 2000. Proceedings. ICRA ’00. IEEE international conference on, vol 2, pp 1023–1029

  46. Urcid G, Valdiviezo JC (2007) Generation of lattice independent vector sets for pattern recognition applications. In: Ritter Gerhard X, Schmalz Mark S, Barrera Junior, Astola Jaakko T (eds) Math. of data/image pattern recog. compression, coding and encrip. with applications X, Proceeding of SPIE 2007, vol 6700, pages 67000C:1–12. SPIE

  47. Villaverde I, D’Anjou A, Graña M (2007) Morphological neural networks and vision based simultaneous localization and maping. Integ Comput Eng 14(4–14):355–363 IOS Press

    Google Scholar 

  48. Villaverde I, Fernandez-Gauna B, Zulueta E (2010) Lattice independent component analysis for mobile robot localization. In: Manhaes-Savio A, Corchado ES, Graña M (eds) Hybrid artificial intelligence systems, vol 6077 of LNAI. Springer, Heidelberg, pp 335–342

  49. Villaverde I, Graña M (2011) Neuro-evolutionary mobile robot egomotion estimation with a 3D ToF camera. Neural Comput Appl 20(3):345–354

    Article  Google Scholar 

  50. Zivkovic Z, Bakker B, Krose B (2005) Hierarchical map building using visual landmarks and geometric constraints. In: Intelligent robots and systems, 2005. (IROS 2005). 2005 IEEE/RSJ international conference on, pp 2480–2485

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Authors and Affiliations

  1. Department of CCIA, UPV/EHU, Computational Intelligence Group, Apdo. 649, 20080, San Sebastian, Spain

    Manuel Graña, Ivan Villaverde, Jose Manuel Lopez-Guede & Borja Fernandez-Gauna

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  1. Manuel Graña
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  2. Ivan Villaverde
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  3. Jose Manuel Lopez-Guede
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  4. Borja Fernandez-Gauna
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Correspondence to Manuel Graña.

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Graña, M., Villaverde, I., Lopez-Guede, J.M. et al. Lattice independent component analysis for appearance-based mobile robot localization. Neural Comput & Applic 21, 1031–1042 (2012). https://doi.org/10.1007/s00521-011-0738-8

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  • DOI: https://doi.org/10.1007/s00521-011-0738-8

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