YaBeSH Engineering and Technology Library

    • Journals
    • PaperQuest
    • YSE Standards
    • YaBeSH
    • Login
    View Item 
    •   YE&T Library
    • ASCE
    • Journal of Computing in Civil Engineering
    • View Item
    •   YE&T Library
    • ASCE
    • Journal of Computing in Civil Engineering
    • View Item
    • All Fields
    • Source Title
    • Year
    • Publisher
    • Title
    • Subject
    • Author
    • DOI
    • ISBN
    Advanced Search
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Archive

    Pose Graph Relocalization with Deep Object Detection and BIM-Supported Object Landmark Dictionary

    Source: Journal of Computing in Civil Engineering:;2023:;Volume ( 037 ):;issue: 005::page 04023020-1
    Author:
    Jack C. P. Cheng
    ,
    Changhao Song
    ,
    Xiao Zhang
    ,
    Zhengyi Chen
    DOI: 10.1061/JCCEE5.CPENG-5301
    Publisher: ASCE
    Abstract: Indoor localization is a prerequisite for autonomous robot applications in the construction industry. However, traditional localization techniques rely on low-level features and do not exploit construction-related semantics. They also are sensitive to environmental factors such as illumination and reflection rate, and therefore suffer from unexpected drifts and failures. This study proposes a pose graph relocalization framework that utilizes object-level landmarks to enhance a traditional visual localization system. The proposed framework builds an object landmark dictionary from Building Information Model (BIM) as prior knowledge. Then a multimodal deep neural network (DNN) is proposed to realize 3D object detection in real time, followed by instance-level object association with false-positive rejection, and relative pose estimation with outlier removal. Finally, a keyframe-based graph optimization is performed to rectify the drifts of traditional visual localization. The proposed framework was validated using a mobile platform with red-green-blue-depth (RGB-D) and inertial sensors, and the test scene was an indoor office environment with furnishing elements. The object detection model achieved 62.9% mean average precision (mAP). The relocalization technique reduced translational drifts by 64.67% and rotational drifts by 41.59% compared with traditional visual–inertial odometry.
    • Download: (4.020Mb)
    • Show Full MetaData Hide Full MetaData
    • Get RIS
    • Item Order
    • Go To Publisher
    • Price: 5000 Rial
    • Statistics

      Pose Graph Relocalization with Deep Object Detection and BIM-Supported Object Landmark Dictionary

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/4293362
    Collections
    • Journal of Computing in Civil Engineering

    Show full item record

    contributor authorJack C. P. Cheng
    contributor authorChanghao Song
    contributor authorXiao Zhang
    contributor authorZhengyi Chen
    date accessioned2023-11-27T23:10:56Z
    date available2023-11-27T23:10:56Z
    date issued5/19/2023 12:00:00 AM
    date issued2023-05-19
    identifier otherJCCEE5.CPENG-5301.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4293362
    description abstractIndoor localization is a prerequisite for autonomous robot applications in the construction industry. However, traditional localization techniques rely on low-level features and do not exploit construction-related semantics. They also are sensitive to environmental factors such as illumination and reflection rate, and therefore suffer from unexpected drifts and failures. This study proposes a pose graph relocalization framework that utilizes object-level landmarks to enhance a traditional visual localization system. The proposed framework builds an object landmark dictionary from Building Information Model (BIM) as prior knowledge. Then a multimodal deep neural network (DNN) is proposed to realize 3D object detection in real time, followed by instance-level object association with false-positive rejection, and relative pose estimation with outlier removal. Finally, a keyframe-based graph optimization is performed to rectify the drifts of traditional visual localization. The proposed framework was validated using a mobile platform with red-green-blue-depth (RGB-D) and inertial sensors, and the test scene was an indoor office environment with furnishing elements. The object detection model achieved 62.9% mean average precision (mAP). The relocalization technique reduced translational drifts by 64.67% and rotational drifts by 41.59% compared with traditional visual–inertial odometry.
    publisherASCE
    titlePose Graph Relocalization with Deep Object Detection and BIM-Supported Object Landmark Dictionary
    typeJournal Article
    journal volume37
    journal issue5
    journal titleJournal of Computing in Civil Engineering
    identifier doi10.1061/JCCEE5.CPENG-5301
    journal fristpage04023020-1
    journal lastpage04023020-18
    page18
    treeJournal of Computing in Civil Engineering:;2023:;Volume ( 037 ):;issue: 005
    contenttypeFulltext
    DSpace software copyright © 2002-2015  DuraSpace
    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