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From Neurons to Robots: Towards Efficient Biologically Inspired Filtering and SLAM

  • Niko Sünderhauf
  • Peter Protzel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6359)

Abstract

We discuss recently published models of neural information processing under uncertainty and a SLAM system that was inspired by the neural structures underlying mammalian spatial navigation. We summarize the derivation of a novel filter scheme that captures the important ideas of the biologically inspired SLAM approach, but implements them on a higher level of abstraction. This leads to a new and more efficient approach to biologically inspired filtering which we successfully applied to real world urban SLAM challenge of 66 km length.

Keywords

Head Direction Visual Odometry Attractor Network Place Recognition Small Prediction Error 
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. 1.
    Bailey, T., Durrant-Whyte, H.: Simultaneous Localisation and Mapping (SLAM): Part II State of the Art. IEEE Robotics and Automation Magazine 13(3), 108–117 (2006)CrossRefGoogle Scholar
  2. 2.
    Beck, J.M., Ma, W.J., Latham, P.E., Pouget, A.: Probabilistic population codes and the exponential family of distributions. Progress in Brain Research 165, 509–519 (2007)CrossRefGoogle Scholar
  3. 3.
    Beierholm, U., Körding, K.P., Shams, L., Ma, W.J.: Comparing bayesian models for multisensory cue combination without mandatory integration. In: NIPS (2007)Google Scholar
  4. 4.
    Durrant-Whyte, H., Bailey, T.: Simultaneous Localisation and Mapping (SLAM): Part I The Essential Algorithms. IEEE Robotics and Automation Magazine 13(2), 99–110 (2006)CrossRefGoogle Scholar
  5. 5.
    Grisetti, G., Stachniss, C., Burgard, W.: Non-linear constraint network optimization for efficient map learning. IEEE Transactions on Intelligent Transportation Systems 10(3), 428–439 (2009)CrossRefGoogle Scholar
  6. 6.
    Ma, W.J., Beck, J.M., Latham, P.E., Pouget, A.: Bayesian inference with probabilistic population codes. Nature Neuroscience 9, 1432–1438 (2006)CrossRefGoogle Scholar
  7. 7.
    Ma, W.J., Beck, J.M., Pouget, A.: Spiking networks for bayesian inference and choice. Current Opinion in Neurobiology 18(2), 217–222 (2008), Cognitive NeuroscienceCrossRefGoogle Scholar
  8. 8.
    Ma, W.J., Pouget, A.: Linking neurons to behavior in multisensory perception: A computational review. Brain Research 1242, 4–12 (2008)CrossRefGoogle Scholar
  9. 9.
    Milford, M.J.: Robot Navigation from Nature. Springer, Heidelberg (March 2008)zbMATHGoogle Scholar
  10. 10.
    Milford, M.J., Wyeth, G.F.: Mapping a Suburb with a Single Camera using a Biologically Inspired SLAM System. IEEE Transactions on Robotics 24(5) (October 2008)Google Scholar
  11. 11.
    Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)zbMATHGoogle Scholar
  12. 12.
    Smets, P.: The combination of evidence in the transferable belief model. IEEE Pattern Analysis and Machine Intelligence 12, 447–458 (1990)CrossRefGoogle Scholar
  13. 13.
    Sünderhauf, N., Neubert, P., Protzel, P.: The Causal Update Filter – A Novel Biologically Inspired Filter Paradigm for Appearance Based SLAM. In: Proc. of the IEEE International Conference on Intelligent Robots and Systems, IROS (2010)Google Scholar
  14. 14.
    Sünderhauf, N., Protzel, P.: Beyond RatSLAM: Improvements to a Biologically Inspired SLAM System. In: Proceedings of the IEEE International Conference on Emerging Technologies and Factory Automation, Bilbao (September 2010)Google Scholar
  15. 15.
    Sünderhauf, N., Protzel, P.: Learning from Nature: Biologically Inspired Robot Navigation and SLAM - A Review. In: Künstliche Intelligenz, German Journal on Artificial Intelligence, Special Issue on SLAM. Springer, Heidelberg (2010)Google Scholar
  16. 16.
    Thrun, Burgard, Fox: Probabilistic Robotics. The MIT Press, Cambridge (2005)zbMATHGoogle Scholar
  17. 17.
    Wilson, R., Finkel, L.: A Neural Implementation of the Kalman Filter. In: Bengio, Y., Schuurmans, D., Lafferty, J., Williams, C.K.I., Culotta, A. (eds.) Advances in Neural Information Processing Systems, vol. 22, pp. 2062–2070 (2009)Google Scholar
  18. 18.
    Zadeh, L.: Fuzzy sets as the basis for a theory of possibility. Fuzzy Sets and Systems 1, 3–28 (1978)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Niko Sünderhauf
    • 1
  • Peter Protzel
    • 1
  1. 1.Department of Electrical Engineering and Information TechnologyChemnitz University of TechnologyChemnitzGermany

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