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Introduction

In all our daily activities, the natural surroundings that we inhabit play a crucial role. Many neurophysiologists have dedicated their efforts towards understanding how our brains can create internal representations of physical space. Both neurobiologists and roboticists are interested in understanding the behaviour of intelligent beings like us and their capacity to learn and use their knowledge of the spatial representation to navigate. The ability of intelligent beings to localize themselves and to find their way back home is linked to their internal “mapping system”. Most navigation approaches require learning and consequently entail memorizing information. Stored information can be organized into cognitive maps - a term introduced for the first time in (Tolman, 1948). Tolman advocates that the animals (rats) do not learn space as a sequence of movements; instead, the animal’s spatial capabilities rest on the construction of maps, which represent the spatial relationships between features in the environment

Keywords

Mobile Robot Place Cell Vertical Edge Colour Patch Partially Observable Markov Decision Process 
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.

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References

  1. Aho, A.V.: Algorithms for finding patterns in strings, pp. 254–300. Elsevier Science Publishers B. V (1990)Google Scholar
  2. Arleo, A., Gerstner, W.: Spatial cognition and neuro-mimetic navigation: a model of hippocampal place cell activity. Biological Cybernetics 83, 287–299 (2000)CrossRefGoogle Scholar
  3. Baeza-Yates, R., Navarro, G.: Faster approximate string matching. Algorithmica 23(2), 127–158 (1999)zbMATHCrossRefMathSciNetGoogle Scholar
  4. Battaglia, F.P., Sutherland, G.R., McNaughton, B.L.: Local sensory cues and place cell directionality: additional evidence of prospective coding in the hippocampus. Journal of Neuroscience 24, 4541–4550 (2004)CrossRefGoogle Scholar
  5. Beeson, P., Jong, K.N., Kuipers, B.: Towards autonomous topological place detection using the extended voronoi graph. In: IEEE International Conference on Robotics and Automaton (ICRA), Barcelona, Spain, pp. 4373–4379 (2005)Google Scholar
  6. Bilmes, J.: A gentle tutorial on the em algorithm and its application to parameter estimation for gaussian mixture and hidden markov models (1997), citeseer.ist.psu.edu/bilmes98gentle.html
  7. Cassandra, R., Kaelbling, A.L.P., Kurien, A.J.: Acting under uncertainty: Discrete bayesian models for mobile-robot navigation. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Osaka, Japan, vol. 2, pp. 963–972 (1996)Google Scholar
  8. Castellanos, J.A., Tardos, J.D.: Mobile Robot Localization and Map Building: Multisensor Fusion Approach. Kluwer Academic Publishers, Dordrecht (1999)Google Scholar
  9. Cho, Y.H., Giese, K.P., Tanila, H.T., Silva, A.J., Eichenbaum, H.: Abnormal hippocampal spatial representations in alphaCaMKIIT286A and CREBalphaDelta- mice. Science 279, 867–869 (1998)CrossRefGoogle Scholar
  10. Choset, H., Nagatani, K.: Topological simultaneous localization and mapping (SLAM): Toward exact localization without explicit localization. IEEE Transactions On Robotics and Automation 17(2), 125–137 (2001)CrossRefGoogle Scholar
  11. Cressant, A., Muller, R.U., Poucet, B.: Remapping of place cells firing patterns after maze rotations. Journal on Experiences on Brain Research 143, 470–479 (2002)CrossRefGoogle Scholar
  12. Dissanayake, M., Newman, M.P., Clark, S., Durrant-Whyte, H., Csorba, M.: A solution to the simultaneous localization and map building (SLAM) problem. IEEE Transactions on Robotics and Automation 17(3), 229–241 (2001)CrossRefGoogle Scholar
  13. Douglas, D., Peucker, T.: Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. The Canadian Cartographer 10(2), 112–122 (1973)Google Scholar
  14. Dufourd, D., Chatila, R., Luzeaux, D.: Combinatorial maps for simultaneous localization and map buiding (SLAM). In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), vol. 2, pp. 1047–1052 (2004)Google Scholar
  15. Gallistel, R.: The Organization of Learning. MIT Press, Cambridge (1990)Google Scholar
  16. Gothard, K.M., Skaggs, W.E., Moore, K.M., McNaughton, B.L.: Binding of hippocampal ca1 neural activity to multiple reference frames in a landmark-based navigation task. Journal of Neuroscience 16, 823–835 (1996)Google Scholar
  17. Hafner, V.V.: Learning places in newly explored environments. In: Publication of the International Society for Adaptive Behavior, Honolulu, USA (2000)Google Scholar
  18. Hartley, T., Burgess, N., Lever, C., Cacucci, F., O’Keefe, J.: Modeling place fields in terms of the cortical inputs to the hippocampus. Hippocampus 10, 369–379 (2000)CrossRefGoogle Scholar
  19. Kali, S., Dayan, P.: The involvement of recurrent connections in area ca3 in establishing the properties of place fields: a model. Hippocampus 20, 7463–7477 (2000)Google Scholar
  20. Kanade, T., Ohta, Y.: Stereo by intra- and inter-scanline search using dynamic programming. IEEE Transactions on Pattern Analysis and Machine Intelligence (PALMZ) 3 (1985)Google Scholar
  21. Kortenkamp, D., Weymouth, T.: Topological mapping for mobile robots using a combination of sonar and vision sensing. In: American Association for Artificial Intelligence (AAAI), Seattle, WA, USA (1994)Google Scholar
  22. Kuipers, B.J.: Modeling spatial knowledge. Cognitive Science 2, 129–153 (1978)CrossRefGoogle Scholar
  23. Lamon, P., Nourbakhsh, I., Jensen, B., Siegwart, R.: Deriving and matching image fingerprint sequences for mobile robot localization. In: IEEE International Conference on Robotics and Automation (ICRA), Seoul, Korea, vol. 2, pp. 1609–1614 (2001)Google Scholar
  24. Lamon, P., Tapus, A., Glauser, E., Tomatis, N., Siegwart, R.: Environmental modeling with fingerprint sequences for topological global localization. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, USA, vol. 4, pp. 3781–3786 (2003)Google Scholar
  25. Leonard, J.J., Durrant-Whyte, H.F.: Directed Sonar Sensing for Mobile Robot Navigation. Kluwer Academic Publisher, Dordrecht (1992)zbMATHGoogle Scholar
  26. Lisien, B., Morales, D., Silver, G., Kantor, D., Rekleitis, I., Choset, H.: Hierarchical simultaneous localization and mapping. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, USA, vol. 1, pp. 448–453 (2003)Google Scholar
  27. Martinelli, A., Tapus, A., Arras, K.O., Siegwart, R.: Multi-resolution slam for real world navigation. In: The 11th International Symposium of Robotics Research (ISRR), Siena, Italy (2003)Google Scholar
  28. Moutarlier, P., Chatila, R.: Stochastic multisensory data fusion for mobile robot location and environment modeling. In: The 5th International Symposium on Robotics Research (ISRR), Tokyo, Japan (1989)Google Scholar
  29. Needleman, S., Wunsch, C.: A general method applicable to the search for similarities in the amino acid sequence of two proteins. Journal on Molecular Biology 48, 443–453 (1970)CrossRefGoogle Scholar
  30. O’Keefe, J., Dostrovsky, J.: The hippocampus as a spatial map. preliminary evidence from unit activity in the freely-moving rat. Journal of Brain Research 34, 171–175 (1971)CrossRefGoogle Scholar
  31. O’Keefe, J., Nadel, L.: The hippocampus as a cognitive map, Clarendon, Oxford (1978)Google Scholar
  32. Owen, C., Nehmzow, U.: Landmark-based navigation for a mobile robot. In: From Animals to Animats: Fifth International Conference on Simulation of Adaptive Behavior (SAB), pp. 240–245. MIT Press, Cambridge (1998)Google Scholar
  33. Redish, D.A.: Beyond the Cognitive Map: From Place Cells to Episodic Memory. MIT Press, Cambridge (1999)Google Scholar
  34. Se, S., Lowe, D., Little, J.: Mobile robot localization and mapping with uncertainty using scale-invariant visual landmarks. International Journal of Robotics Research (IJRR) 21(8), 735–758 (2002)CrossRefGoogle Scholar
  35. Skaggs, E.W., McNaughton, L.B.: Spatial firing properties of hippocampal ca1 populations in an environment containing two visually identical regions. Journal of Neuroscience 18, 8455–8466 (1998)Google Scholar
  36. Smith, C.R., Cheeseman, P.: On the representation and estimation of spatial uncertainty. International Journal of Robotics Research 5(4), 56–68 (1986)CrossRefGoogle Scholar
  37. Tapus, A.: Topological SLAM - Simultaneous Localization and Mapping with Fingerprints of Places. Ph.d thesis, Ecole Politechnique Federale de Lausanne (EPFL), Lausanne, Switzerland (2005)Google Scholar
  38. Tapus, A., Siegwart, R.: Incremental robot mapping with fingerprints of places. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Edmonton, Canada, pp. 2429–2434 (2005)Google Scholar
  39. Tapus, A., Tomatis, N., Siegwart, R.: Topological global localization and mapping with fingerprint and uncertainty. In: The 9th International Symposium on Experimental Robotics (ISER), Singapore, pp. 99–111 (2004)Google Scholar
  40. Thrun, S.: Probabilistic algorithms in robotics. Artificial Intelligence Magazine 21, 93–109 (2000)Google Scholar
  41. Thrun, S.: Learning metric-topological maps for indoor mobile robot navigation. Artificial Intelligence 99(1), 21–71 (1998)zbMATHCrossRefGoogle Scholar
  42. Tolman, C.E.: Cognitive maps in rats and men. Psychological Review 55, 189–208 (1948)CrossRefGoogle Scholar
  43. Tomatis, N., Nourbakhsh, I., Siegwart, R.: Hybrid simultaneous localization and map building: a natural integration of topological and metric. Robotics and Autonomous Systems 44, 3–14 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Adriana Tapus
    • 1
  • Roland Siegwart
    • 2
  1. 1.Interaction LabUniversity of Southern California (USC) 
  2. 2.Autonomous Systems LabSwiss Federal Institute of Technology Zürich (ETHZ) 

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