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Rule Set Based Joint State Update

  • Thilo Grundmann
  • Michael Fiegert
  • Wolfram Burgard
Chapter
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 76)

Abstract

The accurate localization of the objects in the environment is one of the fundamental preconditions for the reliability of service robots. The majority of algorithms for object localization lacks the ability to integrate physical commonsense knowledge into the recognition process especially, when multiple objects are envolved. Consequently the estimates of such methods often do not comply with basic physical constraints such as that rigid objects should not intersect. In this paper, we present an approach for multi-object localization that is able to consider such physical constraints as statistical dependencies in state estimation processes to increase the localization accuracy. Extensive experiments carried out with a real robot in the context of a service robotics scenario demonstrate the practical usefulness of our approach.

Keywords

Ground Truth Information Gain Joint State Observation Model Calibration Pattern 
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 GmbH Berlin Heidelberg 2012

Authors and Affiliations

  • Thilo Grundmann
    • 1
  • Michael Fiegert
    • 1
  • Wolfram Burgard
    • 1
  1. 1.Siemens AGMunichGermany

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