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Spontaneous Reorientation for Self-localization

  • Markus Bader
  • Markus Vincze
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8371)

Abstract

Humanoid robots without internal sensors (e.g. compasses) tend to lose their orientation after a fall or collision. Furthermore, artificial environments are typically rotationally symmetric, causing ambiguities in self-localization. The approach proposed here does not alter the measurement step in the robot’s self-localization. Instead it delivers confidence values for rotationally symmetric poses to the robot’s behaviour controller, which then commands the robot’s self-localization. The behaviour controller uses these confidence values and triggers commands to rearrange the self-localization’s pose beliefs within one measurement cycle. This helps the self-localization algorithm to converge to the correct pose and prevents the algorithm from getting stuck in local minima. Experiments in a symmetric environment with a simulated and a real humanoid NAO robot show that this significantly improves the system.

Keywords

Background Model Humanoid Robot Colour Histogram Real Robot Visual Background 
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 2014

Authors and Affiliations

  • Markus Bader
    • 1
  • Markus Vincze
    • 1
  1. 1.Automation and Control Institute (ACIN)Vienna University of TechnologyViennaAustria

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