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The Bayesian Occupation Filter

  • M. K. Tay
  • Kamel Mekhnacha
  • M. Yguel
  • C. Coué
  • Cédric Pradalier
  • Christian Laugier
  • Th. Fraichard
  • Pierre Bessière
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 46)

Introduction

Perception of and reasoning about dynamic environments is pertinent for mobile robotics and still constitutes one of the major challenges. To work in these environments, the mobile robot must perceive the environment with sensors; measurements are uncertain and normally treated within the estimation framework. Such an approach enables the mobile robot to model the dynamic environment and follow the evolution of its environment. With an internal representation of the environment, the robot is thus able to perform reasoning and make predictions to accomplish its tasks successfully. Systems for tracking the evolution of the environment have traditionally been a major component in robotics. Industries are now beginning to express interest in such technologies. One particular example is the application within the automotive industry for adaptive cruise control (Coué et al., 2002), where the challenge is to reduce road accidents by using better collision detection systems. The major requirement of such a system is a robust tracking system. Most of the existing target-tracking algorithms use an object-based representation of the environment. However, these existing techniques must explicitly consider data association and occlusion. In view of these problems, a grid-based framework, the Bayesian occupancy filter (BOF) (Couéet al., 2002, 2003), has been proposed.

Keywords

Mobile Robot Data Association Adaptive Cruise Control Estimation Step Prediction Step 
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

  • M. K. Tay
    • 1
  • Kamel Mekhnacha
    • 2
  • M. Yguel
    • 1
  • C. Coué
    • 1
  • Cédric Pradalier
    • 3
  • Christian Laugier
    • 1
  • Th. Fraichard
    • 1
  • Pierre Bessière
    • 4
  1. 1.INRIA Rhône-Alpes 
  2. 2.PROBAYES 
  3. 3.CSIRO ICT Centre, Queensland Centre for Advanced technologies (QCAT) 
  4. 4.CNRS - Grenoble Université 

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