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    Effective Map Merging for Long-Term Autonomous Navigation

    Source: Journal of Autonomous Vehicles and Systems:;2024:;volume( 004 ):;issue: 004::page 41001-1
    Author:
    Baird, Christopher
    ,
    Nokleby, Scott
    DOI: 10.1115/1.4066517
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: An algorithm is presented that intelligently merges multiple 3D point clouds used for localization based on when the point cloud was recorded to create an updated map that is more similar to the current environment. The algorithm was implemented on a Boston Dynamics Spot robot and was used to upgrade Spot’s autonomous navigation algorithm called Autowalk by adding the capability for long-term navigation in semi-static environments. The proposed algorithm was validated by having Spot navigate both indoor and outdoor environments over multiple months traveling over 43 km autonomously without losing localization. The proposed method extends the life of programmed autonomous missions to ensure a robot can be used over extended periods of time without the need to re-teach these autonomous missions due to changes in the environment.
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      Effective Map Merging for Long-Term Autonomous Navigation

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4306365
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    contributor authorBaird, Christopher
    contributor authorNokleby, Scott
    date accessioned2025-04-21T10:31:19Z
    date available2025-04-21T10:31:19Z
    date copyright9/27/2024 12:00:00 AM
    date issued2024
    identifier issn2690-702X
    identifier otherjavs_4_4_041001.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4306365
    description abstractAn algorithm is presented that intelligently merges multiple 3D point clouds used for localization based on when the point cloud was recorded to create an updated map that is more similar to the current environment. The algorithm was implemented on a Boston Dynamics Spot robot and was used to upgrade Spot’s autonomous navigation algorithm called Autowalk by adding the capability for long-term navigation in semi-static environments. The proposed algorithm was validated by having Spot navigate both indoor and outdoor environments over multiple months traveling over 43 km autonomously without losing localization. The proposed method extends the life of programmed autonomous missions to ensure a robot can be used over extended periods of time without the need to re-teach these autonomous missions due to changes in the environment.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleEffective Map Merging for Long-Term Autonomous Navigation
    typeJournal Paper
    journal volume4
    journal issue4
    journal titleJournal of Autonomous Vehicles and Systems
    identifier doi10.1115/1.4066517
    journal fristpage41001-1
    journal lastpage41001-11
    page11
    treeJournal of Autonomous Vehicles and Systems:;2024:;volume( 004 ):;issue: 004
    contenttypeFulltext
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    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian
     
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian