With the development of computer vision, the success ratio of underwater vehicle docking has been greatly improved. In this paper, relying on an EPnP visual positioning method, a docking pose estimation approach for autonomous underwater vehicles (AUVs) with extended Kalman filtering (EKF) is proposed. Firstly, the EPnP algorithm is implemented to calculate the relative positioning and posing of AUV to the recovery dock. Then, the EKF is used to minimize the influence of noise and visually measure the point features on the dock. Finally, the simulation of the actual docking environment was verified and attained high-precision experimental results.


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    Title :

    An EPnP Based Extended Kalman Filtering Approach for Docking Pose Estimation of AUVs


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Wu, Meiping (editor) / Niu, Yifeng (editor) / Gu, Mancang (editor) / Cheng, Jin (editor) / Guo, Yuanbo (author) / Bian, Chenyi (author) / Zhang, Yuanxu (author) / Gao, Jian (author)

    Conference:

    International Conference on Autonomous Unmanned Systems ; 2021 ; Changsha, China September 24, 2021 - September 26, 2021



    Publication date :

    2022-03-18


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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