Unlike many urban localization methods that return point-valued estimates, a set-valued representation enables robustness by ensuring that a continuum of possible positions obeys safety constraints. One strategy with the potential for set-valued estimation is GNSS-based shadow matching (SM) in which one uses a three-dimensional (3D) map to compute GNSS shadows (where line-of-sight is blocked). However, SM requires a point-valued grid for computational tractability, with accuracy limited by grid resolution. We propose zonotope shadow matching (ZSM) for set-valued 3D-map-aided GNSS localization. ZSM represents buildings and GNSS shadows using constrained zonotopes, a convex polytope representation that enables propagating set-valued estimates using fast vector concatenation operations. Starting from a coarse set-valued position, ZSM refines the estimate depending on the receiver being inside or outside each shadow as judged by received carrier-to-noise density. We demonstrate our algorithm’s performance using simulated experiments on a simple 3D example map and on a dense 3D map of San Francisco.


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    Titel :

    Set-Valued Shadow Matching Using Zonotopes for 3D-Map-Aided GNSS Localization


    Beteiligte:
    Sriramya Bhamidipati (Autor:in) / Shreyas Kousik (Autor:in) / Grace Gao (Autor:in)


    Erscheinungsdatum :

    2022




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




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