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Filtering by: Creator Billings, Gideon H Remove constraint Creator: Billings, Gideon H Discipline Engineering Remove constraint Discipline: Engineering

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  • UWHandles Dataset

    work
    Creator:
    Billings, Gideon H and Johnson-Roberson, Matthew
    Description:
    UWHandles is a dataset for 6D object pose estimation in underwater fisheye images. It provides 6D pose and 2D bounding box annotations for 3 different graspable handle objects used for ROV manipulation. The dataset consists of 28 image sequences collected in natural seafloor environments with a total of 20,427 annotated frames. and Meta repository for the dataset  https://github.com/gidobot/UWHandles
    Keyword:
    Deep Learning, Pose Estimation, and Underwater Vision
    Citation to related publication:
    Billings, G., & Johnson-Roberson, M. (2020). SilhoNet-fisheye: Adaptation of a ROI based object pose estimation network to monocular fisheye images. IEEE Robotics and Automation Letters, 5(3), 4241-4248.
    Discipline:
    Engineering

  • UWslam Dataset

    work
    Creator:
    Billings, Gideon H and Johnson-Roberson, Matthew
    Description:
    UWslam is a dataset for underwater stereo and hybrid monocular fisheye + stereo SLAM in natural seafloor environments. The dataset includes a spiral survey of a shallow reef captured with a diver operated stereo rig and 4 hybrid image sequences captured with a deep ocean ROV in different deep ocean environments. Ground truth pose estimates for the spiral stereo trajectory were obtained by processing the image sequence through COLMAP. Ground truth pose estimates for the hybrid sequences were obtained by distributing fiducials on the seafloor before capturing an image sequence and processing the image sequences with the ROS based TagSLAM package.
    Keyword:
    SLAM, Simultaneous Localization and Mapping, Visual Reconstruction, and Underwater
    Citation to related publication:
    G. Billings, R. Camilli and M. Johnson-Roberson, "Hybrid Visual SLAM for Underwater Vehicle Manipulator Systems," in IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 6798-6805, July 2022, doi: 10.1109/LRA.2022.3176448.
    Discipline:
    Engineering