In this chapter, we present a fusion approach to localize urban vehicles by integrating visual odometry, a low‐cost GPS, and a 2D digital road map. Distinguished from conventional sensor fusion methods, two types of potential functions (i.e. potential wells and potential trenches) are proposed to represent measurements and constraints, respectively. By choosing different potential functions according to data properties, data from various sensors can be integrated with intuitive understanding, and no extra map matching is required. The minimum of the fused potential, which is regarded as a position estimate, is confined such that fast minimum searching can be achieved. Experiments under realistic conditions have been conducted to validate satisfactory positioning accuracy and robustness compared to pure visual odometry and map‐matching methods.


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    Titel :

    GPS/Odometry/Map Fusion for Vehicle Positioning Using Potential Functions


    Beteiligte:
    Liu, Xinghua (Autor:in) / Jiang, Rui (Autor:in) / Chen, Badong (Autor:in) / Sam Ge, Shuzhi (Autor:in)


    Erscheinungsdatum :

    2022-09-27


    Format / Umfang :

    20 pages




    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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