This paper presents a robust, non-linear smoothing algorithm and develops the theory behind it. This algorithm is extremely robust to outliers and missing data and handles state-dependent noise. Implementing it is straightforward as it consists mainly of two sub-routines: (a) the Rauch-Tung-Striebel recursions, or Kalman smoother; and (b) a backtracking line search strategy. The computational load grows linearly with the number of data because the algorithm preserves the underlying structure of the problem. Global convergence to a local optimum is guaranteed, under mild assumptions.


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    Titel :

    Robust non-linear smoothing for vehicle state estimation


    Beteiligte:
    Agamennoni, Gabriel (Autor:in) / Worrall, Stewart (Autor:in) / Ward, James (Autor:in) / Nebot, Eduardo (Autor:in)


    Erscheinungsdatum :

    01.06.2013


    Format / Umfang :

    492391 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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