• DocumentCode
    3293866
  • Title

    3D reconstruction of underwater structures

  • Author

    Beall, Chris ; Lawrence, Brian J. ; Ila, Viorela ; Dellaert, Frank

  • Author_Institution
    Coll. of Comput. Building, Georgia Inst. of Technol., Atlanta, GA, USA
  • fYear
    2010
  • fDate
    18-22 Oct. 2010
  • Firstpage
    4418
  • Lastpage
    4423
  • Abstract
    Environmental change is a growing international concern, calling for the regular monitoring, studying and preserving of detailed information about the evolution of underwater ecosystems. For example, fragile coral reefs are exposed to various sources of hazards and potential destruction, and need close observation. Computer vision offers promising technologies to build 3D models of an environment from two-dimensional images. The state of the art techniques have enabled high-quality digital reconstruction of large-scale structures, e.g., buildings and urban environments, but only sparse representations or dense reconstruction of small objects have been obtained from underwater video and still imagery. The application of standard 3D reconstruction methods to challenging underwater environments typically produces unsatisfactory results. Accurate, full camera trajectories are needed to serve as the basis for dense 3D reconstruction. A highly accurate sparse 3D reconstruction is the ideal foundation on which to base subsequent dense reconstruction algorithms. In our application the models are constructed from synchronized high definition videos collected using a wide baseline stereo rig. The rig can be hand-held, attached to a boat, or even to an autonomous underwater vehicle. We solve this problem by employing a smoothing and mapping toolkit developed in our lab specifically for this type of application. The result of our technique is a highly accurate sparse 3D reconstruction of underwater structures such as corals.
  • Keywords
    SLAM (robots); computer vision; image reconstruction; image representation; underwater vehicles; 3d image reconstruction; autonomous underwater vehicle; computer vision; dense reconstruction algorithms; sparse representations; underwater ecosystems; underwater structures; underwater video;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference on
  • Conference_Location
    Taipei
  • ISSN
    2153-0858
  • Print_ISBN
    978-1-4244-6674-0
  • Type

    conf

  • DOI
    10.1109/IROS.2010.5649213
  • Filename
    5649213