We present a novel system, AdVENTR for autonomous robot navigation in unstructured outdoor environments that consist of uneven and vegetated terrains. Our approach is general and can enable both wheeled and legged robots to handle outdoor terrain complexity including unevenness, surface properties like poor traction, granularity, obstacle stiffness, etc. We use data from sensors including RGB cameras, 3D Lidar, IMU, robot odometry, and pose information with efficient learning-based perception and planning algorithms that can execute on edge computing hardware. Our system uses a scene-aware switching method to perceive the environment for navigation at any time instant and dynamically switches between multiple perception algorithms. We test our system in a variety of sloped, rocky, muddy, and densely vegetated terrains and demonstrate its performance on Husky and Spot robots.


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    Titel :

    AdVENTR: Autonomous Robot Navigation in Complex Outdoor Environments


    Weitere Titelangaben:

    Springer Proceedings in Advanced Robotics


    Beteiligte:
    Ang Jr, Marcelo H. (Herausgeber:in) / Khatib, Oussama (Herausgeber:in) / Weerakoon, Kasun (Autor:in) / Sathyamoorthy, Adarsh Jagan (Autor:in) / Elnoor, Mohamed (Autor:in) / Manocha, Dinesh (Autor:in)

    Kongress:

    International Symposium on Experimental Robotics ; 2023 ; Melia, Chiang Mai, Thailand November 26, 2023 - November 30, 2023


    Erschienen in:

    Experimental Robotics ; Kapitel : 19 ; 219-228


    Erscheinungsdatum :

    2024-08-06


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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