Safe navigation with simultaneous localization and mapping (SLAM) for autonomous robots is crucial in challenging environments. To achieve this goal, detecting moving objects in the surroundings and building a static map are essential. However, existing moving object segmentation methods have been developed separately for each field, making it challenging to perform real-time navigation and precise static map building simultaneously. In this paper, we propose an integrated real-time framework that combines online tracking-based moving object segmentation with static map building. For safe navigation, we introduce a computationally efficient hierarchical association cost matrix to enable real-time moving object segmentation. In the context of precise static mapping, we present a voting-based method, DS-Voting, designed to achieve accurate dynamic object removal and static object recovery by emphasizing their spatio-temporal differences. We evaluate our proposed method quantitatively and qualitatively in the SemanticKITTI dataset and real-world challenging environments. The results demonstrate that dynamic objects can be clearly distinguished and incorporated into static map construction, even in stairs, steep hills, and dense vegetation.


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    Titel :

    TOSS: Real-Time Tracking and Moving Object Segmentation for Static Scene Mapping


    Weitere Titelangaben:

    Lect. Notes in Networks, Syst.


    Beteiligte:
    Abdul Majeed, Anwar P.P. (Herausgeber:in) / Yap, Eng Hwa (Herausgeber:in) / Liu, Pengcheng (Herausgeber:in) / Huang, Xiaowei (Herausgeber:in) / Nguyen, Anh (Herausgeber:in) / Chen, Wei (Herausgeber:in) / Kim, Ue-Hwan (Herausgeber:in) / Jang, Seoyeon (Autor:in) / Oh, Minho (Autor:in) / Yu, Byeongho (Autor:in)

    Kongress:

    International Conference on Robot Intelligence Technology and Applications ; 2023 ; Taicang December 06, 2023 - December 08, 2023



    Erscheinungsdatum :

    22.11.2024


    Format / Umfang :

    13 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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