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Dimensions of Uncertainty in Evidential Grid Maps

  • Thomas Reineking
  • Joachim Clemens
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8684)

Abstract

We show how a SLAM algorithm based on belief function theory can produce evidential occupancy grid maps that provide a mobile robot with additional information about its environment. While uncertainty in probabilistic grid maps is usually measured by entropy, we show that for evidential grid maps, uncertainty can be expressed in a three-dimensional space and we propose appropriate measures for quantifying uncertainty in these different dimensions. We analyze these measures in a practical mapping example containing typical sources of uncertainty for SLAM. As a result of the evidential representation, the robot is able to distinguish between different sources of uncertainty (e.g., a lack of measurements vs. conflicting measurements) which are indistinguishable in the probabilistic framework.

Keywords

Mobile Robot Belief State Belief Function Sensor Model Evidential Representation 
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 International Publishing Switzerland 2014

Authors and Affiliations

  • Thomas Reineking
    • 1
  • Joachim Clemens
    • 1
  1. 1.Cognitive NeuroinformaticsUniversity of BremenGermany

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