Journal of Intelligent & Robotic Systems

, Volume 76, Issue 1, pp 151–167 | Cite as

Vessel Inspection: A Micro-Aerial Vehicle-based Approach

  • Alberto Ortiz
  • Francisco Bonnin-Pascual
  • Emilio Garcia-Fidalgo
Article

Abstract

Vessel maintenance entails periodic visual inspections of the internal and external parts of the hull in order to detect the typical defective situations affecting metallic structures, such as coating breakdown, corrosion, cracks, etc. The main goal of project MINOAS is the automation of the inspection process, currently undertaken by human surveyors, by means of a fleet of robotic agents. This paper overviews an approach to the inspection problem based on an autonomous Micro Aerial Vehicle (MAV) which, as part of this fleet, is in charge of regularly supplying images that can teleport the surveyor from a base station to the areas of the hull to be inspected. The control software approach adopted for the MAV is fully described, with a special emphasis on the self-localization capabilities of the vehicle. Experimental results showing the suitability of the platform to the application are as well reported and discussed.

Keywords

MAV Visual odometry Visual inspection 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Achtelik, M., Achtelik, M., Weiss, S., Siegwart, R.: Onboard IMU and monocular vision based control for MAVs in unknown in- and outdoor environments. In: Intl. Conf. Robotics and Automation (2011)Google Scholar
  2. 2.
    Achtelik, M., Bachrach, A., He, R., Prentice, S., Roy, N.: Autonomous navigation and exploration of a quadrotor helicopter in GPS-denied indoor environments. In: Intl. Aerial Robotics Competition (2009)Google Scholar
  3. 3.
    Achtelik, M.W., Lynen, S., Weiss, S., Kneip, L., Chli, M., Siegwart, R.: Visual-inertial SLAM for a small helicopter in large outdoor environments. In: Intl. Conf. Intell. Robots and Systems (2012)Google Scholar
  4. 4.
    Bachrach, A., Prentice, S., He, R., Roy, N.: RANGE-Robust autonomous navigation in GPS-denied environments. J. Field Robotics 28(5), 644–666 (2011)CrossRefGoogle Scholar
  5. 5.
    Bay, H., Tuytelaars, T., Gool, L.V.: SURF: Speeded up robust features. In: European Conf. Computer Vision (2006)Google Scholar
  6. 6.
    Bonnin-Pascual, F., Garcia-Fidalgo, E., Ortiz, A.: Semi-autonomous visual inspection of vessels assisted by an unmanned micro aerial vehicle. In: Intl. Conf. Intell. Robots and Systems (2012)Google Scholar
  7. 7.
    Bouabdallah, S., Murrieri, P., Siegwart, R.: Towards Autonomous Indoor Micro VTOL. Auton. Robot. 18, 171–183 (2005)CrossRefGoogle Scholar
  8. 8.
    Calonder, M., Lepetit, V., Ozuysal, M., Trzcinski, T., Strecha, C., Fua, P.: BRIEF: computing a local binary descriptor very fast. Trans. Pattern Anal. Mach. Intell. 34(7), 1281–1298 (2012)CrossRefGoogle Scholar
  9. 9.
    Castillo, P., Lozano, R., Dzul, A.: Modelling and control of mini-flying machines. Advances in Industrial Control. Springer (2005)Google Scholar
  10. 10.
    Chowdhary, G., Johnson, E.N., Magree, D., Wu, A., Shein, A.: GPS-denied indoor and outdoor monocular vision aided navigation and control of unmanned aircraft. J. Field Robotics 30(3), 415–438 (2013)CrossRefGoogle Scholar
  11. 11.
    Dryanovski, I., Valenti, R.G., Xiao, J.: An open-source navigation system for micro aerial vehicles. Auton. Robot. 34(3), 177–188 (2013)CrossRefGoogle Scholar
  12. 12.
    Eberli, D., Scaramuzza, D., Weiss, S., Siegwart, R.: Vision based position control for MAVs using one single circular landmark. J. Intel. Robotic Systems 61(1–4), 495–512 (2011)CrossRefGoogle Scholar
  13. 13.
    Engel, J., Sturm, J., Cremers, D.: Camera-based navigation of a low-cost quadrocopter. In: Intl. Conf. Intell. Robots and Systems (2012)Google Scholar
  14. 14.
    Fraundorfer, F., Heng, L., Honegger, D., Lee, G.H., Lorenz, Meier, Tanskanen, P., Pollefeys, M.: Vision-based autonomous mapping and exploration using a quadrotor MAV. In: Intl. Conf. Intell. Robots and Systems (2012)Google Scholar
  15. 15.
    Grisetti, G., Stachniss, C., Burgard, W.: Improved techniques for grid mapping with rao-blackwellized particle filters. Trans. Robotics 23(1), 34–46 (2007)CrossRefGoogle Scholar
  16. 16.
    Grzonka, S., Grisetti, G., Burgard, W.: A fully autonomous indoor quadrotor. Trans. Robotics 28(1), 90–100 (2012)CrossRefGoogle Scholar
  17. 17.
    Gurdan, D., Stumpf, J., Achtelik, M., Doth, K.M., Hirzinger, G., Rus, D.: Energy-efficient autonomous four-rotor flying robot controlled at 1 khz. In: Intl. Conf. Robotics and Automation (2007)Google Scholar
  18. 18.
    He, R., Prentice, S., Roy, N.: Planning in information space for a quadrotor helicopter in a GPS-denied environment. In: Intl. Conf. Robotics and Automation (2008)Google Scholar
  19. 19.
    Honegger, D., Meier, L., Tanskanen, P., Pollefeys, M.: An open source and open hardware embedded metric optical flow CMOS camera for indoor and outdoor applications. In: Intl. Conf. Robotics and Automation (2013)Google Scholar
  20. 20.
    Lee, G., Achtelik, M., Fraundorfer, F., Pollefeys, M., Siegwart, R.: A benchmarking tool for MAV visual pose estimation. In: Intl. Conf. Control, Automation, Robotics and Vision (2010)Google Scholar
  21. 21.
    Li, W., Zhang, T., Kuhnlenz, K.: A vision-guided autonomous quadrotor in an air-ground multi-robot system. In: Intl. Conf. Robotics and Automation (2011)Google Scholar
  22. 22.
    Lourakis, M.: levmar: Levenberg–Marquardt non-linear least squares algorithms in C/C+ +. http://www.ics.forth.gr/~lourakis/levmar/. Accessed 24 July 2011
  23. 23.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Intl. J. Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  24. 24.
    Matsue, A., Hirosue, W., Tokutake, H., Sunada, S., Ohkura, A.: Navigation of small and lightweight helicopter. Trans. Japan Soc. for Aeronautical and Space Sciences 48(161), 177–179 (2005)CrossRefGoogle Scholar
  25. 25.
    Meier, L., Tanskanen, P., Heng, L., Lee, G.H., Fraundorfer, F., Pollefeys, M.: PIXHAWK: a micro aerial vehicle design for autonomous flight using onboard computer vision. Auton. Robot. 33(1–2), 21–39 (2012)CrossRefGoogle Scholar
  26. 26.
    Newsome, S., Rodocker, J.: Effective technology for underwater hull and infrastructure inspection: the SeaBotix LBC. In: Oceans Conf. (2009)Google Scholar
  27. 27.
    Ortiz, A., Bonnin, F., Gibbins, A., Apostolopoulou, P., Bateman, W., Eich, M., Spadoni, F., Caccia, M., Drikos, L.: First steps towards a roboticized visual inspection system for vessels. In: Intl. Conf. Emerging Technologies and Factory Automation (2010)Google Scholar
  28. 28.
    Ortiz, A., Bonnin-Pascual, F., Garcia-Fidalgo, E.: Vessel inspection assistance by means of a micro-aerial vehicle: control architecture and self-localization issues. Tech. Rep. A-02-2013, Dep. of Mathematics and Comp. Science - UIB (2013). http://dmi.uib.es/aortiz/publ/tr0213.pdf
  29. 29.
    Ortiz, R.: FREAK: fast retina keypoint. In: Conf. Computer Vision and Pattern Recognition (2012)Google Scholar
  30. 30.
    Roberts, J.F., Stirling, T., Zufferey, J.C., Floreano, D.: Quadrotor using minimal sensing for autonomous indoor flight. In: Proc. European Micro Air Vehicle Conf. and Flight Competition (2007)Google Scholar
  31. 31.
    Rosten, E., Drummond, T.: Fusing points and lines for high performance tracking. In: Intl. Conf. Computer Vision (2005)Google Scholar
  32. 32.
    ROTISII: Publishable Final Activity Report. http://cordis.europa.eu/projects/index.cfm?fuseaction=app.details&REF=74284. Accessed 10 Dec 2012
  33. 33.
    Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to SIFT or SURF. In: Intl. Conf. Computer Vision (2011)Google Scholar
  34. 34.
    Wenzel, K.E., Masselli, A., Zell, A.: Automatic Take Off, Tracking and Landing of a Miniature UAV on a Moving Carrier Vehicle. J. Intel. Robotic Systems 61(1–4), 221–238 (2011)CrossRefGoogle Scholar
  35. 35.
    Zingg, S., Scaramuzza, D., Weiss, S., Siegwart, R.: MAV navigation through indoor corridors using optical flow. In: Intl. Conf. Robotics and Automation (2010)Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Alberto Ortiz
    • 1
  • Francisco Bonnin-Pascual
    • 1
  • Emilio Garcia-Fidalgo
    • 1
  1. 1.Department of Mathematics and Computer ScienceUniversity of Balearic IslandsPalma de MallorcaSpain

Personalised recommendations