Probabilistic map learning: Necessity and difficulties

  • P. Hébert
  • S. Betgé-Brezetz
  • R. Chatila
Accepted Papers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1093)


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.


mobile robot map learning probabilistic approach geometric features extended Kalman filtering critical observations 


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Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • P. Hébert
    • 1
  • S. Betgé-Brezetz
    • 1
  • R. Chatila
    • 1
  1. 1.LAAS-CNRSToulouse CedexFrance

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