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Neural network applications in sensor fusion for an autonomous mobile robot

  • Joris W. M. van Dam
  • Ben J. A. Kröse
  • Franciscus C. A. Groen
Accepted Papers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1093)

Abstract

In this article, we propose a generic architecture for sensor data fusion and argue that the central issue in such an approach is the choice of a suitable representation of the robot's environment. We argue that for the navigation task a robot-centered discrete probabilistic representation (an occupancy grid) is a suitable choice. If such a representation is used, the two key problems are how to transform such representations upon robot motion and how to represent the sensor's error characteristics (the sensor model) in such a representation. For both these problems, solutions are suggested by the application of neural network theory, and it is argued that these neural networks are the best available alternatives.

keywords

sensor fusion neural networks occupancy grids transformation of occupancy grids learning sensor models 

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Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Joris W. M. van Dam
    • 1
  • Ben J. A. Kröse
    • 1
  • Franciscus C. A. Groen
    • 1
  1. 1.Faculty of Mathematics and Computer ScienceUniversity of AmsterdamSJ Amsterdam

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