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)


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.


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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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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