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Experiments in Visual Localisation around Underwater Structures

  • Stephen Nuske
  • Jonathan Roberts
  • David Prasser
  • Gordon Wyeth
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 62)

Abstract

Localisation of an AUV is challenging and a range of inspection applications require relatively accurate positioning information with respect to submerged structures. We have developed a vision based localisation method that uses a 3D model of the structure to be inspected. The system comprises a monocular vision system, a spotlight and a low-cost IMU. Previous methods that attempt to solve the problem in a similar way try and factor out the effects of lighting. Effects, such as shading on curved surfaces or specular reflections, are heavily dependent on the light direction and are difficult to deal with when using existing techniques. The novelty of our method is that we explicitly model the light source. Results are shown of an implementation on a small AUV in clear water at night.

Keywords

Real Image Inertial Measurement Unit Autonomous Underwater Vehicle Synthetic Image Visual Localisation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Blicher, A.P., Roy, S., Penev, P.S.: Lightsphere: Fast lighting compensation for matching a 2d image to a 3d model. In: 17th International Conference on (ICPR 2004), pp. 157–162. IEEE Computer Society, Washington (2004)Google Scholar
  2. 2.
    Blinn, J.F.: Models of light reflection for computer synthesized pictures. SIGGRAPH Comput. Graph. 11(2), 192–198 (1977)CrossRefGoogle Scholar
  3. 3.
    Dunbabin, M., Roberts, J., Usher, K., Winstanley, G., Corke, P.: A hybrid AUV design for shallow water reef navigation. In: Proceedings of the 2005 IEEE International Conference on Robotics and Automation, ICRA 2005, pp. 2105–2110 (2005)Google Scholar
  4. 4.
    Gerard, P., Gagalowicz, A.: Three dimensonal model-based tracking using texture learning and matching. Pattern Recognition Letters 21, 1095–1103 (2000)zbMATHCrossRefGoogle Scholar
  5. 5.
    Ho, N., Jarvis, R.: Global localisation in real and cyber worlds using vision. In: Australasian Conference on Robotics and Automation (2007)Google Scholar
  6. 6.
    Intel: Open Source Computer Vision Library: Reference Manual (2000), http://www.intel.com/technology/computing/opencv
  7. 7.
    Kee, S.C., Lee, K.M., Lee, S.U.: Illumination invariant face recognition using photometric stereo. IEICE Trans. on Information and Systems 7, 1466–1474 (2000)Google Scholar
  8. 8.
    Klein, G., Murray, D.: Full-3d edge tracking with a particle filter. In: British Machine Vision Conference, pp. 1119–1128 (2006)Google Scholar
  9. 9.
    Kondo, H., Maki, T., Ura, T., Nose, Y., Sakamaki, T., Inaishi, M.: Relative navigation of an autonomous underwater vehicle using a light-section profiling system. In: Proceedings of the 2005 IEEE International Conference on Intelligent Robots and Systems, IROS, pp. 1103–1108 (2004)Google Scholar
  10. 10.
    Kondo, H., Ura, T., Nose, Y., Akizono, J., Sakai, H.: Visual investigation of underwater structures by the AUV and sea trials. In: Proceedings of OCEANS 2003, vol. 1, pp. 340–345 (2003)Google Scholar
  11. 11.
    Marchand, E., Bouthemy, P., Chaumette, F.: A 2d-3d model-based approach to real-time visual tracking. Image and Vision Computing 19(13), 941–955 (2001)CrossRefGoogle Scholar
  12. 12.
    Noyer, J., Lanvin, P., Benjelloun, M.: Model-based tracking of 3d objects based on a sequential monte-carlo method. In: Conference on Signals, Systems and Computers, vol. 2, pp. 1744–1748 (2004)Google Scholar
  13. 13.
    Stolkin, R., Hodgetts, M., Greig, A.: An em / e-mrf strategy for underwater navigation. In: Proceedings of the British Machine Vision Conference, pp. 715–724 (2000)Google Scholar
  14. 14.
    Thrun, S., Burgard, W., Fox, D.: Probabalistic Robotics. The MIT Press, Cambridge (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Stephen Nuske
    • 1
  • Jonathan Roberts
    • 2
  • David Prasser
    • 2
  • Gordon Wyeth
    • 3
  1. 1.The Robotics InstituteCarnegie Mellon UniversityPittsburghUSA
  2. 2.Autonomous Systems LabCSIRO ICT CentreKenmoreAustralia
  3. 3.School of Information Technology and Electrical EngineeringUniversity of QueenslandSt LuciaAustralia

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