Autonomous Landing of MAVs on an Arbitrarily Textured Landing Site Using Onboard Monocular Vision

  • Shaowu Yang
  • Sebastian A. Scherer
  • Konstantin Schauwecker
  • Andreas Zell
Article

Abstract

This paper presents a novel solution for micro aerial vehicles (MAVs) to autonomously search for and land on an arbitrary landing site using real-time monocular vision. The autonomous MAV is provided with only one single reference image of the landing site with an unknown size before initiating this task. We extend a well-known monocular visual SLAM algorithm that enables autonomous navigation of the MAV in unknown environments, in order to search for such landing sites. Furthermore, a multi-scale ORB feature based method is implemented and integrated into the SLAM framework for landing site detection. We use a RANSAC-based method to locate the landing site within the map of the SLAM system, taking advantage of those map points associated with the detected landing site. We demonstrate the efficiency of the presented vision system in autonomous flights, both indoor and in challenging outdoor environment.

Keywords

Micro aerial vehicles Autonomous navigation Autonomous landing SLAM Monocular vision 

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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: Proceedings 2011 the IEEE International Conference on Robotics and Automation (2011)Google Scholar
  2. 2.
    Bloesch, M., Weiss, S., Scaramuzza, D., Siegwart, R.: Vision based MAV navigation in unknown and unstructured environments. In: Proceedings 2010 IEEE International Conference on Robotics and Automation, Anchorage, AK, pp. 21–28 (2010)Google Scholar
  3. 3.
    Bouguet, J.Y.: Camera Calibration Toolbox for Matlab. http://www.vision.caltech.edu/bouguetj/calib_doc (2004)
  4. 4.
    Castle, R.O., Klein, G., Murray, D.W.: Combining monoSLAM with object recognition for scene augmentation using a wearable camera. Image Vis. Comput. 28(11), 1548–1556 (2010)CrossRefGoogle Scholar
  5. 5.
    Castle, R.O., Murray, D.W.: Keyframe-based recognition and localization during video-rate parallel tracking and mapping. Image Vis. Comput. 29(8), 524–532 (2011). doi:10.1016/j.imavis.2011.05.002 CrossRefGoogle Scholar
  6. 6.
    Bradski, G.: The OpenCV Library. Dr. Dobb’s Journal of Software Tools (2000)Google Scholar
  7. 7.
    Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF: binary robust independent elementary features. In: European Conference on Computer Vision (2010)Google Scholar
  8. 8.
    Cesetti, A., Frontoni, E., Mancini, A., Zingaretti, P., Longhi, S.: A vision-based guidance system for UAV navigation and safe landing using natural landmarks. J. Intell. Robot. Syst. 57(1–4), 233–257 (2010)CrossRefMATHGoogle Scholar
  9. 9.
    Davison, A.J.: Real-time simultaneous localisation and mapping with a single camera. In: Proc 9th IEEE Int. Conf. on Computer Vision, pp. 1403–1410 (2003)Google Scholar
  10. 10.
    Fraundorfer, F., Heng, L., Honegger, D., Lee, G., Tanskanen, P., Pollefeys, M.: Vision-based autonomous mapping and exploration using a quadrotor MAV. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2012)Google Scholar
  11. 11.
    Garcia-Pardoa, P.J., Sukhatmeb, G.S., Montgomery, J.F.: Towards vision-based safe landing for an autonomous helicopter. Robot. Auton. Syst. 38(1), 19–29 (2002)CrossRefGoogle Scholar
  12. 12.
    Grzonka, S., Grisetti, G., Burgard, W.: Towards a navigation system for autonomous indoor flying. In: 2009 IEEE International Conference on Robotics and Automation, pp. 2878–2883 (2009)Google Scholar
  13. 13.
    Klein, G., Murray, D.: Parallel tracking and mapping for small AR workspaces. In: International Symposium on Mixed and Augmented Reality, pp. 225–234 (2007)Google Scholar
  14. 14.
    Lu, H., Zheng, Z.: Two novel real-time local visual features for omnidirectional vision. Pattern Recog. 43(12), 3938–3949 (2010)CrossRefMATHGoogle Scholar
  15. 15.
    Lu, H., Zhang, H., Yang, S., Zheng, Z.: Camera parameters auto-adjusting technique for robust robot vision. In: 2010 IEEE International Conference on Robotics and Automation (ICRA), pp. 1518–1523 (2010)Google Scholar
  16. 16.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRefGoogle Scholar
  17. 17.
    Mahony, R., Kumar, V., Corke, P.: Multirotor aerial vehicles: modeling, estimation, and control of quadrotor. IEEE Robot. Autom. Mag. 19(3), 20–32 (2012)CrossRefGoogle Scholar
  18. 18.
    Mondragón, I.F., Campoy, P., Martínez, C., Olivares-Méndez, M.A.: 3D pose estimation based on planar object tracking for UAVs control. In: 2010 IEEE International Conference on Robotics and Automation (ICRA), pp. 35–41 (2010)Google Scholar
  19. 19.
    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
  20. 20.
    Mellinger, D., Michael, N., Kumar, V.: Trajectory generation and control for precise aggressive maneuvers with quadrotors. Int. J. Robot. Res. 31(5), 664–674 (2012)CrossRefGoogle Scholar
  21. 21.
    Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to SIFT or SURF. In: 2011 IEEE International Conference on Computer Vision (ICCV) (2011)Google Scholar
  22. 22.
    Quigley, M., Gerkey, B., Conley, K., Faust, J., Foote, T., Leibs, J., Berger, E., Wheeler, R., Ng, A.: ROS: an open-source robot operating system. In: Open-Source Software Workshop of the Int. Conf. on Robotics and Automation, Kobe, Japan (2009)Google Scholar
  23. 23.
    Rosten, E., Drummond, T.: Machine learning for high-speed corner detection. In: Proc. 9th European Conference on Computer Vision (ECCV’06), Graz (2006)Google Scholar
  24. 24.
    Saripalli, S., Montgomery, J.F., Sukhatme, G.S.: Visually guided landing of an unmanned aerial vehicle. IEEE Trans. Robot. Autom. 19(3), 371–380 (2003)CrossRefGoogle Scholar
  25. 25.
    Scherer, S.A., Dube, D., Komma, P., Masselli, A., Zell, A.: Robust real-time number sign detection on a mobile outdoor robot. In: Proceedings of the 6th European Conference on Mobile Robots (ECMR 2011), Orebro, Sweden (2011)Google Scholar
  26. 26.
    Scherer, S.A., Dube, D., Zell, A.: Using depth in visual simultaneous localisation and mapping. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 5216–5221 (2012)Google Scholar
  27. 27.
    Schauwecker, K., Ke, N.R., Scherer, S.A., Zell, A.: Markerless visual control of a quad-rotor micro aerial vehicle by means of on-board stereo processing. In: Autonomous Mobile System Conference (AMS), pp. 11–20 Springer (2012)Google Scholar
  28. 28.
    Shen, S., Michael, N., Kumar, V.: Autonomous multi-floor indoor navigation with a computationally constrained MAV. In: 2011 IEEE International Conference on Robotics and Automation, pp. 20–25 (2011)Google Scholar
  29. 29.
    Weiss, S., Scaramuzza, D., Siegwart, R.: Monocular-SLAM-based navigation for autonomous micro helicopters in GPS-denied environments. Field Robot. 28(6), 854–874 (2011)CrossRefGoogle Scholar
  30. 30.
    Yang, S., Scherer, S.A., Zell, A: An onboard monocular vision system for autonomous takeoff, hovering and landing of a micro aerial vehicle. J. Intell. Robot. Syst. 69(1–4), 499–515 (2013)CrossRefGoogle Scholar
  31. 31.
    Yang, S., Scherer, S.A., Schauwecker, K., Zell, A.: Onboard monocular vision for landing of an MAV on a landing site specified by a single reference image. In: 2013 International Conference on Unmanned Aircraft Systems (ICUAS’13), pp. 317–324 (2013)Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Shaowu Yang
    • 1
  • Sebastian A. Scherer
    • 1
  • Konstantin Schauwecker
    • 1
  • Andreas Zell
    • 1
  1. 1.Department of Computer ScienceUniversity of TübingenTübingenGermany

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