Estimating the configuration of a vehicle is crucial for navigation. The most classical approaches are (extended) Kalman filtering and Markov localization, often implemented via particle filtering. Interval analysis allows an alternative approach: bounded-error localization. Contrary to classical Extended Kalman Filtering, this approach allows global localisation, and contrary to Markov localization it provides guaranteed results in the sense that a set is computed that contains all of the configurations that are consistent with the data and hypotheses. This paper describes the bounded-error localization algorithms so as to present a complexity study and how to achieve a real time implementation.


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    Title :

    Guaranteed state estimation tuning for real time applications


    Contributors:


    Publication date :

    2009-06-01


    Size :

    1118438 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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