Modern automotive vehicles are often equipped with a budget commercial rolling shutter (RS) camera. These devices usually produce distorted images due to the camera's inter-row delay while capturing the image. They will defeat the following computer vision tasks, such as ego-motion estimation. In this paper, we present a closed-form solver for a vehicle-mounted RS camera ego-motion estimation problem with only one point-match between two consecutive distorted RS images, called RS-1 pt. To the best of our knowledge, this is the first work to address the ego-motion estimation problem for vehicle-mounted RS cameras specifically. Thanks to the 1 pt minimal solver, extensive experiments demonstrate the proposed method surpasses classical global shutter based (GS-based) solvers and existing RS-based solvers by a significant margin in terms of both efficiency and effectiveness. Besides, we synthesize and self-collect three vehicle-mounted RS camera datasets with ground-truth GS images and trajectory, which are absent from the existing works and will be publicly available as a benchmark to the research community.


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    Titel :

    Ego-Motion Estimation for Vehicles With a Rolling Shutter Camera


    Beteiligte:
    Zhang, Yongcong (Autor:in) / Liao, Bangyan (Autor:in) / Qu, Delin (Autor:in) / Wu, Junde (Autor:in) / Lu, Xinyu (Autor:in) / Li, Wanting (Autor:in) / Xue, Yifei (Autor:in) / Lao, Yizhen (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.03.2025


    Format / Umfang :

    8105825 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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