This paper proposes a real-time vehicle location method based on multi-source information fusion of vehicle wheel speed features and visual images for the problems of weak texture features, high similarity of regional features, low accuracy of feature recognition and detection, and jump of vehicle location results in ship scene. Firstly, in order to solve the problem of inertial navigation sensor failed and get accurate pose estimation of vehicle motion, the article builds a vehicle motion prediction model based on vehicle wheel speed features. Secondly, according to the significant geometric features of the actual scene, Hough transform, edge extraction, template matching and inverse projection transform algorithms are used to identify and detect visual features of the scene. Thirdly, an extended Kalman filter is used to fuse vehicle wheel speed pose estimation and visual features to identify and detect the results to obtain reliable and high-precision real-time location of the vehicle. Finally, experiments are employed to verify the real-time vehicle location method based on multi-source information fusion of vehicle wheel speed features and visual images. The experimental results show that this method can effectively achieve vehicle localization in weak texture, similar regional features, and non-stationary road scenes.


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    Titel :

    Ship scene vehicle location method based on multi-source information fusion


    Beteiligte:
    Pan, Wentao (Autor:in) / Guo, Jianpo (Autor:in) / Dai, Zongmiao (Autor:in) / Wang, Weiru (Autor:in)

    Kongress:

    Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022) ; 2022 ; Chongqing,China


    Erschienen in:

    Proc. SPIE ; 12566


    Erscheinungsdatum :

    28.03.2023





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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