The probability-hypothesis-density simultaneous localization and mapping filter is a random-finite-set estimation method that incorporates the probability-hypothesis-density filter within a Rao-Blackwellized particle filter, and was developed for navigation and mapping problems. However, the filter tends to diverge due to the existing importance-weighting methods used in the Rao-Blackwellized particle filter. This article introduces a new importance-weighting method that drastically improves the robustness of the probability-hypothesis-density simultaneous localization and mapping filter. Performance evaluations are conducted using both simulations and real experimental data sets.


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    Titel :

    Multifeature-based importance weighting for the PHD SLAM filter


    Beteiligte:


    Erscheinungsdatum :

    01.12.2016


    Format / Umfang :

    4804152 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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