RGB-D Simultaneous Localization and Mapping (SLAM) in indoor environments is a hot topic in computer vision and robotics communities, and the dynamic environment is a remaining problem. Dynamic environments, which are often caused by dynamic humans in indoor environments, usually lead to the camera pose tracking method failure, feature association error or loop closure failure. In this paper, we propose a robust dense RGB-D SLAM method which efficiently detects humans and fast reconstructs the static backgrounds in the dynamic human environments. By using the deep learning-based human body detection method, we first quickly recognize the human body joints in the current RGB frame, even when the body is occluded. We then apply graph-based segmentation on the 3D point clouds, which separates the detected moving humans from the static environments. Finally, the left static environment is aligned with a state-of-the-art frame-to-model scheme. Experimental results on common RGB-D SLAM benchmark show that the proposed method achieves outstanding performance in dynamic environments. Moreover, it is even comparable to the performance of the related state-of-the-art methods in static environments.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    PoseFusion: Dense RGB-D SLAM in Dynamic Human Environments


    Weitere Titelangaben:

    Springer Proceedings in Advanced Robotics


    Beteiligte:
    Xiao, Jing (Herausgeber:in) / Kröger, Torsten (Herausgeber:in) / Khatib, Oussama (Herausgeber:in) / Zhang, Tianwei (Autor:in) / Nakamura, Yoshihiko (Autor:in)

    Kongress:

    International Symposium on Experimental Robotics ; 2018 ; Buenos Aires, Argentina November 05, 2018 - November 08, 2018



    Erscheinungsdatum :

    2020-01-23


    Format / Umfang :

    9 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    PoseFusion: Dense RGB-D SLAM in Dynamic Human Environments

    Zhang, Tianwei / Nakamura, Yoshihiko | TIBKAT | 2020


    Multi-object Monocular SLAM for Dynamic Environments

    Nair, Gokul B. / Daga, Swapnil / Sajnani, Rahul et al. | IEEE | 2020


    Evaluation of SLAM Algorithms for Highly Dynamic Environments

    Roesler, Oliver / Ravindranath, Vignesh Padubidri | TIBKAT | 2020


    VI-SLAM for Subterranean Environments

    Kramer, Andrew / Kasper, Mike / Heckman, Christoffer | TIBKAT | 2021


    D3VIL-SLAM: 3D Visual Inertial LiDAR SLAM for Outdoor Environments

    Frosi, Matteo / Matteucci, Matteo | IEEE | 2023