On-Board Dual-Stereo-Vision for the Navigation of an Autonomous MAV

Article

Abstract

We present a quadrotor Micro Aerial Vehicle (MAV) equipped with four cameras, which are arranged in two stereo configurations. The MAV is able to perform stereo matching for each camera pair on-board and in real-time, using an efficient sparse stereo method. In case of the camera pair that is facing forward, the stereo matching results are used for a reduced stereo SLAM system. The other camera pair, which is facing downwards, is used for ground plane detection and tracking. Hence, we are able to obtain a full 6DoF pose estimate from each camera pair, which we fuse with inertial measurements in an extended Kalman filter. Special care is taken to compensate various drift errors. In an evaluation we show that using two instead of one camera pair significantly increases the pose estimation accuracy and robustness.

Keywords

Micro Aerial Vehicle (MAV) Unmanned Aerial Vehicle (UAV) Stereo vision Robot vision Simultaneous Localization And Mapping (SLAM) Parallel Tracking And Mapping (PTAM) Sensor fusion 

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References

  1. 1.
    Achtelik, M., Zhang, T., Kuhnlenz, K., Buss, M.: Visual tracking and control of a quadcopter using a stereo camera system and inertial sensors. In: International Conference on Mechatronics and Automation (ICMA), pp. 2863–2869. IEEE (2009)Google Scholar
  2. 2.
    Achtelik, M., Achtelik, M., Weiss, S., Siegwart, R.: Onboard IMU and monocular vision based control for MAVs in unknown in-and outdoor environments. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 3056–3063 (2011)Google Scholar
  3. 3.
    Benhimane, S., Malis, E.: Real-time image-based tracking of planes using efficient second-order minimization. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), vol. 1, pp. 943–948 (2004)Google Scholar
  4. 4.
    Bry, A., Bachrach, A., Roy, N.: State estimation for aggressive flight in GPS-denied environments using onboard sensing. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 1–8 (2012)Google Scholar
  5. 5.
    Carrillo, L.R.G., López, A.E.D., Lozano, R., Pégard, C.: Combining stereo vision and inertial navigation system for a quad-rotor UAV. J. Intell. Robot. Syst. 65(1), 373–387 (2012)CrossRefGoogle Scholar
  6. 6.
    Engel, J., Sturm, J., Cremers, D.: Camera-based navigation of a low-cost quadrocopter. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2815–2821 (2012)Google Scholar
  7. 7.
    Fraundorfer, F., Heng, L., Honegger, D., Lee, G.H., Meier, L., Tanskanen, P., Pollefeys, M.: Vision-based autonomous mapping and exploration using a quadrotor MAV. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4557–4564 (2012)Google Scholar
  8. 8.
    Harmat, A., Sharf, I., Trentini, M.: Parallel tracking and mapping with multiple cameras on an unmanned aerial vehicle. In: International Conference on Intelligent Robotics and Applications (ICIRA), vol. 1, pp. 421–432. Springer (2012)Google Scholar
  9. 9.
    Heng, L., Meier, L., Tanskanen, P., Fraundorfer, F., Pollefeys, M.: Autonomous obstacle avoidance and maneuvering on a vision-guided MAV using on-board processing. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 2472–2477 (2011)Google Scholar
  10. 10.
    Klein, G., Murray, D.: Parallel tracking and mapping for small AR workspaces. In: IEEE and ACM International Symposium on Mixed and Augmented Reality (ISMAR), pp. 1–10 (2007)Google Scholar
  11. 11.
    Klose, S.: imu_filter. http://ros.org/wiki/imu_filter (2011)
  12. 12.
    Meier, L., Tanskanen, P., Heng, L., Lee, G., Fraundorfer, F., Pollefeys, M.: PIXHAWK: a micro aerial vehicle design for autonomous flight using onboard computer vision. Auton. Robot. 1–19 (2012)Google Scholar
  13. 13.
    Pebrianti, D., Kendoul, F., Azrad, S., Wang, W., Nonami, K.: Autonomous hovering and landing of a quad-rotor micro aerial vehicle by means of on ground stereo vision system. J. Syst. Des. Dyn. 4(2), 269–284 (2010)Google Scholar
  14. 14.
    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
  15. 15.
    Schauwecker, K., Klette, R., Zell, A.: A new feature detector and stereo matching method for accurate high-performance sparse stereo matching. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5171–5176 (2012)Google Scholar
  16. 16.
    Schauwecker, K., Zell, A.: On-board dual-stereo-vision for autonomous quadrotor navigation. In: International Conference on Unmanned Aircraft Systems (ICUAS), pp. 332–341. IEEE (2013)Google Scholar
  17. 17.
    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
  18. 18.
    Shen, S., Michael, N., Kumar, V.: Autonomous multi-floor indoor navigation with a computationally constrained MAV. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 20–25 (2011)Google Scholar
  19. 19.
    Shen, S., Michael, N., Kumar, V.: Autonomous indoor 3D exploration with a micro-aerial vehicle. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 9–15 (2012)Google Scholar
  20. 20.
    Tomic, T., Schmid, K., Lutz, P., Domel, A., Kassecker, M., Mair, E., Grixa, I., Ruess, F., Suppa, M., Burschka, D.: Toward a fully autonomous UAV: research platform for indoor and outdoor urban search and rescue. IEEE Robot. Autom. Mag. 19(3), 46–56 (2012)CrossRefGoogle Scholar
  21. 21.
    Tournier, G.P., Valenti, M., How, J., Feron, E.: Estimation and control of a quadrotor vehicle using monocular vision and Moiré patterns. In: In AIAA Guidance, Navigation and Control Conference, pp. 2006–6711 (2006)Google Scholar
  22. 22.
    Weiss, S., Scaramuzza, D., Siegwart, R.: Monocular-SLAM–based navigation for autonomous micro helicopters in GPS-denied environments. J. Field Robot. 28(6), 854–874 (2011)CrossRefGoogle Scholar
  23. 23.
    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, 499–515 (2012)CrossRefGoogle Scholar
  24. 24.
    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: International Conference on Unmanned Aircraft Systems (ICUAS), pp. 317–324. IEEE (2013)Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Computer Science Department, Cognitive SystemsUniversity of TübingenTübingenGermany

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