Visual odometry and mapping methods can provide accurate navigation and comprehensive environment (obstacle) information for autonomous flights of Unmanned Aerial Vehicle (UAV) in GPS-denied cluttered environments. This work presents a new light small-scale low-cost ARM-based stereo vision pre-processing system, which not only is used as onboard sensor to continuously estimate 6-DOF UAV pose, but also as onboard assistant computer to pre-process visual information, thereby saving more computational capability for the onboard host computer of the UAV to conduct other tasks. The visual odometry is done by one plugin specifically developed for this new system with a fixed baseline (12cm). In addition, the pre-processed infromation from this new system are sent via a Gigabit Ethernet cable to the onboard host computer of UAV for real-time environment reconstruction and obstacle detection with a octree-based 3D occupancy grid mapping approach, i.e. OctoMap. The visual algorithm is evaluated with the stereo video datasets from EuRoC Challenge III in terms of efficiency, accuracy and robustness. Finally, the new system is mounted and tested on a real quadrotor UAV to carry out the visual odometry and mapping task.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Efficient visual odometry and mapping for Unmanned Aerial Vehicle using ARM-based stereo vision pre-processing system


    Beteiligte:
    Fu, Changhong (Autor:in) / Carrio, Adrian (Autor:in) / Campoy, Pascual (Autor:in)


    Erscheinungsdatum :

    01.06.2015


    Format / Umfang :

    4980958 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Stereo Visual Inertial Odometry for Unmanned Aerial Vehicle Autonomous Flight

    Liu, Quanpan / Wang, Zhengjie / Wang, Huan | British Library Conference Proceedings | 2020


    Stereo Visual Inertial Odometry for Unmanned Aerial Vehicle Autonomous Flight

    Liu, Quanpan / Wang, Zhengjie / Wang, Huan | Springer Verlag | 2019


    Large scale visual odometry using stereo vision

    Hernández-Gutiérrez, Andrés / Nieto, Juan I. / Vidal-Calleja, Teresa A. et al. | BASE | 2009

    Freier Zugriff

    Visual-Inertial Odometry for Unmanned Aerial Vehicle using Deep Learning

    Lee, Hongyun / McCrink, Matthew / Gregory, James W. | AIAA | 2019


    Reliability of visual inertial odometry on an unmanned aerial vehicle

    Setati Tiro / Maweni Thabisa / Botha Natasha | DOAJ | 2023

    Freier Zugriff