• DocumentCode
    2341752
  • Title

    Analysis of local observability for feature localization in a maritime environment using an omnidirectional camera

  • Author

    Xu, Bin ; Stilwell, Daniel J. ; Gadre, Aditya S. ; Kurdila, Andrew

  • Author_Institution
    Virginia Polytech. Inst. & State Univ., Blacksburg
  • fYear
    2007
  • fDate
    Oct. 29 2007-Nov. 2 2007
  • Firstpage
    3666
  • Lastpage
    3671
  • Abstract
    Autonomous operation by a surface vehicle in a maritime setting requires that the surface vehicle detects non-water objects, including shoreline, hazards to navigation, and other moving vessels. In order to assess the utility of an omnidirectional camera for detecting and localizing non- water objects, we rigorously investigate observability of both stationary and moving features. For stationary features, we find that all but a small subset of the features are observable. For moving features, we show that an important class of feature and ASV trajectories are not observable.
  • Keywords
    Kalman filters; marine vehicles; mobile robots; nonlinear control systems; observability; remotely operated vehicles; robot kinematics; robot vision; ASV kinematics; Kalman filters; autonomous surface vehicle operation; feature localization; local observability analysis; maritime environment; moving features; nonlinear systems; nonwater object detection; omnidirectional camera; stationary features; Cameras; Hazards; Instruments; Mobile robots; Navigation; Object detection; Observability; Remotely operated vehicles; Simultaneous localization and mapping; Vehicle detection;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Robots and Systems, 2007. IROS 2007. IEEE/RSJ International Conference on
  • Conference_Location
    San Diego, CA
  • Print_ISBN
    978-1-4244-0912-9
  • Electronic_ISBN
    978-1-4244-0912-9
  • Type

    conf

  • DOI
    10.1109/IROS.2007.4399483
  • Filename
    4399483