In this paper, we present an Adaptive Feature Optimization Strategy as a novel frontend feature selection strategy which can be applied in the most direct method-based Simultaneous Localization And Mapping (SLAM) system for high accuracy, efficiency and robustness compared to the original SLAM system. It chooses adaptively the ORB points or Direct Sparse Odometry (DSO)-based points for tracking depending on in which scenario the cameras situate. Our evaluation on public datasets presents that the SLAM system integrated with our strategy outperforms the state-of-the-art which significantly reduces the processing time for each frame while retains the tracking accuracy.


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    Titel :

    An Adaptive Feature Optimization Strategy for Direct Visual Odometry


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Wu, Meiping (Herausgeber:in) / Niu, Yifeng (Herausgeber:in) / Gu, Mancang (Herausgeber:in) / Cheng, Jin (Herausgeber:in) / Deng, Junyuan (Autor:in) / Pei, Ling (Autor:in) / Wu, Qi (Autor:in) / Li, Tao (Autor:in) / Chen, Xin (Autor:in) / Yu, Wenxian (Autor:in)

    Kongress:

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



    Erscheinungsdatum :

    18.03.2022


    Format / Umfang :

    12 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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