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

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KI 2010: Advances in Artificial Intelligence (KI 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6359))

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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.

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering and Information Technology, Chemnitz University of Technology, 09126, Chemnitz, Germany

    Niko Sünderhauf & Peter Protzel

Authors
  1. Niko Sünderhauf
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  2. Peter Protzel
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Editor information

Editors and Affiliations

  1. Institute for Anthropomatics, KIT, Karlsruhe, Germany

    Rüdiger Dillmann

  2. Lehrstuhl für Interaktive Echtzeitsysteme, KIT, Karlsruhe, Germany

    Jürgen Beyerer

  3. Lehrstuhl für Intelligente Sensor-Aktor-Systeme (ISAS), KIT, Karlsruhe, Germany

    Uwe D. Hanebeck

  4. Cognitive Systems Lab (CSL), KIT, Karlsruhe, Germany

    Tanja Schultz

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© 2010 Springer-Verlag Berlin Heidelberg

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Sünderhauf, N., Protzel, P. (2010). From Neurons to Robots: Towards Efficient Biologically Inspired Filtering and SLAM. In: Dillmann, R., Beyerer, J., Hanebeck, U.D., Schultz, T. (eds) KI 2010: Advances in Artificial Intelligence. KI 2010. Lecture Notes in Computer Science(), vol 6359. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16111-7_39

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  • DOI: https://doi.org/10.1007/978-3-642-16111-7_39

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-16110-0

  • Online ISBN: 978-3-642-16111-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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