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Probabilistic map learning: Necessity and difficulties

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Reasoning with Uncertainty in Robotics (RUR 1995)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1093))

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Abstract

In the context of map learning, a mobile robot must build and maintain a representation of the environment incrementally while locating itself. The robot is equipped with a set of sensors of limited precisions and may have an inexact model of the system evolution. The representation model is probabilistic in nature and the EKF (Extended Kalman Filtering) algorithm has been widely adopted to model and propagate uncertainty in both the position of the robot and the geometric features.

We present an analysis of the EKF algorithm. The formalism and some of the main approaches are reviewed. An important aspect of the analysis is concerned with the necessity and the difficulties to maintain correlation between the state variables (the geometric features, the robot). The effects of nonlinearities on uncertainty propagation and the degradation of the sensors' uncertainty models are analysed and illustrated through simulation examples.

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Author information

Authors and Affiliations

  1. LAAS-CNRS, 7, avenue du Colonel-Roche, Toulouse Cedex, France

    P. Hébert, S. Betgé-Brezetz & R. Chatila

Authors
  1. P. Hébert
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  2. S. Betgé-Brezetz
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  3. R. Chatila
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Editor information

Leo Dorst Michiel van Lambalgen Frans Voorbraak

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© 1996 Springer-Verlag Berlin Heidelberg

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Hébert, P., Betgé-Brezetz, S., Chatila, R. (1996). Probabilistic map learning: Necessity and difficulties. In: Dorst, L., van Lambalgen, M., Voorbraak, F. (eds) Reasoning with Uncertainty in Robotics. RUR 1995. Lecture Notes in Computer Science, vol 1093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0013969

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  • DOI: https://doi.org/10.1007/BFb0013969

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61376-3

  • Online ISBN: 978-3-540-68506-7

  • eBook Packages: Springer Book Archive

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