• DocumentCode
    1648383
  • Title

    Visual mapping of internal pipe walls using sparse features for application on board Autonomous Underwater Vehicles

  • Author

    Bodenmann, Adrian ; Thornton, Blair ; Ura, Tamaki ; Painumgal, Unnikrishnan V.

  • Author_Institution
    Lab. de Syst. Robotiques, Ecole Polytech. Fed. de Lausanne, Lausanne, Switzerland
  • fYear
    2009
  • Firstpage
    1
  • Lastpage
    8
  • Abstract
    In this project an algorithm to generate a single image of an entire water pipe´s inside wall was developed. An Autonomous Underwater Vehicle (AUV) equipped with a fish-eye camera will be deployed to dive through the pipe and take pictures of its inside wall. The algorithm was implemented that maps such pictures to the three dimensional model of the water pipe and based on that, generates the image that one would see if the photographed section of pipe was cut along the side and rolled out. The algorithm then uses the rolled out versions of a series of consecutively taken photos to generate a mosaic showing the entire pipe in a single picture based on the recognition of sparse, structural features. The performance of the algorithm was verified in land based experiments and the robustness of the system to measurement inaccuracy was assessed. Finally two mosaicked images were created for different paths taken through the pipe, to demonstrate that the system is capable of generating the same image, regardless of the path taken by the AUV.
  • Keywords
    image segmentation; mobile robots; pipes; remotely operated vehicles; underwater vehicles; 3D model; autonomous underwater vehicle; fish-eye camera; internal pipe walls; mosaicked image; sparse feature; structural feature; visual mapping; water pipe; Cameras; Image generation; Inspection; Laboratories; Pipelines; Position measurement; Remotely operated vehicles; Robot kinematics; Robot vision systems; Underwater vehicles;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    OCEANS 2009 - EUROPE
  • Conference_Location
    Bremen
  • Print_ISBN
    978-1-4244-2522-8
  • Electronic_ISBN
    978-1-4244-2523-5
  • Type

    conf

  • DOI
    10.1109/OCEANSE.2009.5278231
  • Filename
    5278231