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Bayesian Maps: Probabilistic and Hierarchical Models for Mobile Robot Navigation

  • Julien Diard
  • Pierre Bessière
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 46)

Introduction

Imagine yourself lying in your bed at night. Now try to answer these questions: Is your body parallel or not to the sofa that is two rooms away from your bedroom? What is the distance between your bed and the sofa? Except for some special cases (like rotating beds, people who actually sleep on their sofas, or tiny apartments), these questions are usually nontrivial, and answering them requires abstract thought. If pressed to answer quickly, so as to forbid the use of abstract geometry learned in high school, the reader will very probably give wrong answers.

However, if people had the same representations of their environment that roboticians usually provide to their robots, answering these questions would be very easy. The answers would come quickly, and they would certainly be correct. Indeed, robotic representations of space are usually based on large-scale, accurate, metric Cartesian maps. This enables judgment of parallelism and estimations of distances to be straightforward.

Keywords

Mobile Robot Markov Decision Process Navigation Task Mobile Robot Navigation Abstraction Operator 
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 2008

Authors and Affiliations

  • Julien Diard
    • 1
  • Pierre Bessière
    • 2
  1. 1.Laboratoire de Psychologie et NeuroCognition CNRS UMR 5105Université Pierre Mendès France, BSHM 
  2. 2.CNRS - Grenoble Université 

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