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Journal of Intelligent & Robotic Systems

, Volume 73, Issue 1–4, pp 401–412 | Cite as

Framework for Autonomous On-board Navigation with the AR.Drone

  • Jacobo Jiménez Lugo
  • Andreas Zell
Article

Abstract

We present a framework for autonomous flying using the AR.Drone low cost quadrotor. The system performs all sensing and computations on-board, making the system independent of any base station or remote control. High level navigation, computer vision and control tasks are carried out in an external processing unit that steers the vehicle to a desired location. We experimentally demonstrate the properties and capabilities of three systems to autonomously following several trajectory patterns, visually estimate its position and detecting and following a person and evaluate the performance of the systems.

Keywords

Micro aerial vehicle Computer vision Low cost Pose estimation Autonomous navigation Quadrotor 

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References

  1. 1.
    Lupashin, S., Schöllig, A., Sherback, M., D’Andrea, R.: A simple learning strategy for high-speed quadrocopter multi-flips. In: IEEE International Conference on Robotics and Automation, pp. 1642–1648 (2010)Google Scholar
  2. 2.
    Muller, M., Lupashin, S., D’Andrea, R.: Quadrocopter ball juggling. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5113–5120 (2011)Google Scholar
  3. 3.
    Mellinger, D., Michael, N., Kumar, V.: Trajectory generation and control for precise aggressive manuevers with quadrotors. Int. J. Robot. Res. 31(5), 664–674 (2012)Google Scholar
  4. 4.
    Hoffmann, G., Rajnarayan, D.G., Waslander, S.L., Dostal, D., Jang, J.S., Tomlin, C.J.: The Stanford testbed of autonomous rotorcraft for multi-agent control (STARMAC). In: 23rd Digital Avionics Systems Conference, Salt Lake City (2004)Google Scholar
  5. 5.
    Meier, L., Tanskanen, P., Fraundorfer, F., Pollefeys, M.: Pixhawk: a system for autonomous flight using onboard computer vision. In: IEEE International Conference on Robotics and Automation (ICRA), 2011, pp. 2992–2997 (2011)Google Scholar
  6. 6.
    Lim, H., Park, J., Lee, D., Kim, H.J.: Build your own quadrotor: open-source projects on unmanned aerial vehicles. IEEE Robot. Automat. Mag. 19(3), 33–45 (2012)CrossRefGoogle Scholar
  7. 7.
    Tayebi, A., McGilvray, S., Roberts, A., Moallem, M.: Attitude estimation and stabilization of a rigid body using low-cost sensors. In: 46th IEEE Conference on Decision and Control, 2007, pp. 6424–6429 (2007)Google Scholar
  8. 8.
    Wenzel, K., Masselli, A., Zell, A.: Automatic take off, tracking and landing of a miniature uav on a moving carrier vehicle. J. Intell. Robot. Syst. 61, 221–238 (2011). doi: 10.1007/s10846-010-9473-0 CrossRefGoogle Scholar
  9. 9.
    Wenzel, K.E., Masselli, A., Zell, A.: Visual tracking and following of a quadrocopter by another quadrocopter. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4993–4998 (2012). doi: 10.1109/IROS.2012.6385635
  10. 10.
    Saska, M., Krajník, T., Faigl, J., Vonásek, V., Preucil, L.: Low cost mav platform ar-drone in experimental verifications of methods for vision based autonomous navigation. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2012), pp. 4808–4809 (2012)Google Scholar
  11. 11.
    Faigl, J., Krajník, T., Vonásek, V., Přeučil, L.: Surveillance planning with localization uncertainty for UAVs. In: 3rd Israeli Conference on Robotics, Ariel (2010)Google Scholar
  12. 12.
    Bills, C., Chen, J., Saxena, A.: Autonomous mav flight in indoor environments using single image perspective cues. In: IEEE International Conference on Robotics and Automation, pp. 5776–5783 (2011)Google Scholar
  13. 13.
    Engel, J., Sturm, J., Cremers, D.: Camera-based navigation of a low-cost quadrocopter. In: Proc. of the International Conference on Intelligent Robot Systems (IROS) (2012)Google Scholar
  14. 14.
    Klein, G., Murray, D.: Parallel tracking and mapping for small AR workspaces. In: Proc. Sixth IEEE and ACM International Symposium on Mixed and Augmented Reality (ISMAR’07), Nara, Japan (2007)Google Scholar
  15. 15.
    Jimenez Lugo, J., Zell, A.: Framework for autonomous onboard navigation with the ar.drone. In: 2013 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 575–583 (2013)Google Scholar
  16. 16.
    Bristeau, P.-J., Callou, F., Vissiere, D., Petit, N.: The navigation and control technology inside the AR.Drone micro uav. In: 18th IFAC World Congress, pp. 1477–1484, Milano, Italy (2011)Google Scholar
  17. 17.
    Trajkovic, M., Hedley, M.: Fast corner detection. Image Vis. Comput. 16(2), 75–87 (1998)CrossRefGoogle Scholar
  18. 18.
    Masselli, A., Zell, A.: A novel marker based tracking method for position and attitude control of MAVs. In: Proceedings of International Micro Air Vehicle Conference and Flight Competition, pp. 1–6, DGON, Braunschweig (2012)Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Cognitive SystemsUniversity of TübingenTübingenGermany

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