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Progress towards underwater 3D scene recovery

Published:19 May 2010Publication History

ABSTRACT

The underwater environment presents many challenges for robotic systems and sensors. Not only is it difficult to determine appropriate locomotive and control strategies, sensing underwater is plagued by highly variable lighting, dynamic objects, and suspended particulate matter. Despite these challenges the aquatic environment presents many real and practical applications for autonomous robots. Fundamentally, these tasks require knowledge of the 3D environment, the robot's location within the environment, and reactive vehicle control. In this paper we describe solutions to the problem of providing effective control of underwater robotic systems that can be used to obtain accurate models of underwater structures. Two specific components of the research are described here: (i) a 3D stereo-vision sensor that integrates stereo vision imagery with inertial measurements, and (ii) a tablet-based control interface that can be used to control the process of data collection.

References

  1. J. W. Bales and C. Chryssostomidis. High bandwidth, low-power, short-range optical communication underwater. In Proc. 9th Int. Symp. on Unmanned Untethered Submersible Technology, 1995.Google ScholarGoogle Scholar
  2. L. Berkhovskikh and Y. Lysanov. Fundamentals of Ocean Acoustics. Springer, New York, 1982.Google ScholarGoogle ScholarCross RefCross Ref
  3. G. Dudek, J. Sattar, and A. Xu. A visual language for robot control and programming: a human-interface study. In IEEE ICRA, pages 2507--2513, Rome, Italy, 2007.Google ScholarGoogle ScholarCross RefCross Ref
  4. M. Fischler and R. Bolles. Random sample consensus: a paradigm for model fitting with application to image analysis and automated cartography. Communications of the ACM, 24:381--385, 1981. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. A. German and M. Jenkin. Gait synthesis for legged underwater vehicles. In Proc. ICAS 2009, Valencia, Spain, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. S. Gill and M. Jenkin. Polygonal meshing for 3D stereo video sensor data. In Proc. Computer and Robot Vision, Windsor, ON, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. A. Hogue. SensorSLAM: an investigation into the utilization of sensor parameters within the SLAM framework. PhD thesis, York University, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. A. Hogue, A. German, J. Zacher, and M. Jenkin. Underwater 3D mapping: experiences and lessons learned. In Third Canadian Conference on Computer and Robot Vision, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. B. Horn. Closed-form solution of absolute orientation using unit quaternions. Journal of the Optical Society of America A, 4:629--642, 1987.Google ScholarGoogle ScholarCross RefCross Ref
  10. B. Lucas and T. Kanade. An iterative image registration technique with an application to stereo vision. In IJCAI, pages 674--679, 1981. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. W. Press, S. Teukolsky, W. Vetterling, and B. Flannery. Numerical Recipies in C. Cambridge University Press, 2002.Google ScholarGoogle Scholar
  12. A. Quazi and W. Konrad. Underwater acoustic communication. IEEE Comm. Magazine, pages 24--29, March 1982.Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. T. Shaneyfelt, M. A. Joordens, K. Nagothu, and M. Jamshidi. RF communication between surface and underwater robotic teams. In World Automatic Congress (WAC), pages 1--6, Hawaii, 2008.Google ScholarGoogle Scholar
  14. J. Shi and C. Tomasi. Good features to track. In IEEE CVPR, pages 593--600, 1994.Google ScholarGoogle Scholar
  15. A. Topol, M. Jenkin, J. Gyrz, S. Wilson, M. Kwietniewski, P. Jasiobedzki, H.-K. Ng, and M. Bondy. Generating semantic information from 3D scans of crime scenes. In Proc. Computer and Robot Vision, Windsor, ON, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. B. Triggs, P. McLauchlan, R. Harley, and A. Fitzgibbon. Bundle adjustment - a modern synthesis. Lecture Notes in Computer Science, Volume 1883, pages 298--372, 1999. Proc. of the International Workshop on Vision Algorithms: Theory and Practice. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. B. Verzijlenberg. 6DOF pose estimation using 3D sensors. MSc Thesis. Computer Science. York University. (In Preparation.), 2010.Google ScholarGoogle Scholar
  18. B. Verzijlenberg and M. Jenkin. Swimming with robots: human robot communication at depth. Submitted for publication, 2010.Google ScholarGoogle Scholar

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  1. Progress towards underwater 3D scene recovery

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          cover image ACM Conferences
          C3S2E '10: Proceedings of the Third C* Conference on Computer Science and Software Engineering
          May 2010
          156 pages
          ISBN:9781605589015
          DOI:10.1145/1822327

          Copyright © 2010 ACM

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          Publication History

          • Published: 19 May 2010

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