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    Fusing Vision and Inertial Sensors for Robust Runway Detection and Tracking

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    Name:
    JGCD-G002898_AUTHOR_1 (1).pdf
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    3.413Mb
    Format:
    PDF
    Description:
    Accepted Manuscript
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    Type
    Article
    Authors
    Abu Jbara, Khaled F. cc
    Sundaramoorthi, Ganesh cc
    Claudel, Christian
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering Program
    Visual Computing Center (VCC)
    Date
    2018-06-30
    Online Publication Date
    2018-06-30
    Print Publication Date
    2018-09
    Permanent link to this record
    http://hdl.handle.net/10754/628829
    
    Metadata
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    Abstract
    This work presents a novel real-time algorithm for runway detection and tracking applied to unmanned aerial vehicles (UAVs). The algorithm relies on a combination of segmentation-based region competition and minimization of a particular energy function to detect and identify the runway edges from streaming video data. The resulting video-based runway position estimates can be updated using a Kalman filter (KF) that integrates additional kinematic estimates such as position and attitude angles, derived from video, inertial measurement unit data, or positioning data. This allows a more robust tracking of the runway under turbulence. The performance of the proposed lane detection and tracking scheme is illustrated on various experimental UAV flights conducted by the Saudi Aerospace Research Center (KACST), by the University of Texas, Austin, and on simulated landing videos obtained from a flight simulator. Results show an accurate tracking of the runway edges during the landing phase, under various lighting conditions, even in the presence of roads, taxiways, and other obstacles. This suggests that the positional estimates derived from the video data can significantly improve the guidance of the UAV during takeoff and landing phases.
    Citation
    Abu-Jbara K, Sundaramorthi G, Claudel C (2018) Fusing Vision and Inertial Sensors for Robust Runway Detection and Tracking. Journal of Guidance, Control, and Dynamics 41: 1929–1946. Available: http://dx.doi.org/10.2514/1.G002898.
    Sponsors
    We gratefully acknowledge the Aerospace Division of King Abdulaziz City for Science and Technology (KACST) for supporting us with experimental flight videos generated by the SAKER 4 UAV.
    Publisher
    American Institute of Aeronautics and Astronautics (AIAA)
    Journal
    Journal of Guidance, Control, and Dynamics
    DOI
    10.2514/1.G002898
    Additional Links
    https://arc.aiaa.org/doi/10.2514/1.G002898
    ae974a485f413a2113503eed53cd6c53
    10.2514/1.G002898
    Scopus Count
    Collections
    Articles; Electrical and Computer Engineering Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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