Journal of Intelligent & Robotic Systems

, Volume 84, Issue 1–4, pp 351–369 | Cite as

Exploration and Mapping Technique Suited for Visual-features Based Localization of MAVs

  • Jan Chudoba
  • Miroslav Kulich
  • Martin Saska
  • Tomáš Báča
  • Libor Přeučil
Article

Abstract

An approach for long term localization, stabilization, and navigation of micro-aerial vehicles (MAVs) in unknown environment is presented in this paper. The proposed method relies strictly on onboard sensors of employed MAVs and does not require any external positioning system. The core of the method consists in extraction of information from pictures consequently captured using a camera carried by the particular MAV. Visual features are obtained from images of the surface under the MAV, and stored into a map that is represented by these features. The position of the MAV is then obtained through matching with previously stored features. An important part of the proposed system is a novel approach for exploration and mapping of the workspace of robots. This method enables efficient exploring of the unknown environment, while keeping the iteratively built map of features consistent. The proposed algorithm is suitable for mapping of surfaces, both outdoor and indoor, with various density of the image features. The sufficient precision and long term persistence of the method allows its utilization for stabilization of large MAV groups that work in formations with small relative distances between particular vehicles. Numerous experiments with quadrotor helicopters and various numerical simulations have been realized for verification of the entire system and its components.

Keywords

MAVs Visual-features MAV localization MAV stabilization Exploration Mapping 

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References

  1. 1.
    Vicon motion systems: http://www.vicon.com (2014)
  2. 2.
    Amidi, O.: An Autonomous Vision-Guided Helicopter. PhD thesis, Carnegie Mellon University, Department of Electrical and Computer Engineering Pittsburgh, PA 15213 (1996)Google Scholar
  3. 3.
    Francesco, A., Vincenzo, C.: An information-based exploration strategy for environment mapping with mobile robots. Robot. Auton. Syst. 58(5), 684–699 (2010)CrossRefGoogle Scholar
  4. 4.
    Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: SURF: Speeded up robust features. Comput. Vis. Image Underst. 110(3), 346–359 (2008)CrossRefGoogle Scholar
  5. 5.
    Besl, P. J., McKay, N.D.: A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14, 239–256 (1992)CrossRefGoogle Scholar
  6. 6.
    Blosch, Michael, Weiss, Stephan, Scaramuzza, Davide, Siegwart, Roland: Vision based mav navigation in unknown and unstructured environments. In: Robotics and automation (ICRA), 2010 IEEE international conference on, pages 21–28. IEEE (2010)Google Scholar
  7. 7.
    Caballero, F., Merino, L., Ferruz, J., Ollero, A.: Vision-based odometry and slam for medium and high altitude flying uavs. J. Intell. Robot. Syst. 54(1–3), 137–161 (2009)CrossRefGoogle Scholar
  8. 8.
    Caron, Francois, Duflos, Emmanuel, Pomorski, Denis, Vanheeghe, Philippe: Gps/imu data fusion using multisensor kalman filtering: introduction of contextual aspects. Information Fusion 7(2), 221–230 (2006)CrossRefGoogle Scholar
  9. 9.
    Rodolfo, L., Carrillo, G., Enrique, A., López, D., Lozano, R., Pégard, C.: Combining stereo vision and inertial navigation system for a quad-rotor uav. J. Intell. Robot. Syst. 65(1-4), 373–387 (2012)CrossRefGoogle Scholar
  10. 10.
    Conte, G., Doherty, P.: An integrated uav navigation system based on aerial image matching. IEEE (2008)Google Scholar
  11. 11.
    Conte, G., Doherty, P.: Vision-based unmanned aerial vehicle navigation using geo-referenced EURASIP Journal on Advances in Signal Processing. Special section p1, 2009 (2009)MATHGoogle Scholar
  12. 12.
    Dijkstra, E. W.: A note on two problems in connexion with graphs. Numer. Math. 1(1), 269–271 (1959)MathSciNetCrossRefMATHGoogle Scholar
  13. 13.
    Fowers, S.G.: Stabilization and control of a quad-rotor micro-uav using vision sensors (2008)Google Scholar
  14. 14.
    Gonzalez-Banos, H. H., Latombe, J.-C.: Navigation strategies for exploring indoor environments. Int. J. Robot. Res. 21(10-11), 829–848 (2002)CrossRefGoogle Scholar
  15. 15.
    Grabe, V., Bulthoff, H. H., Giordano, P. R.: Robust optical-flow based self-motion estimation for a quadrotor uav Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on, pages 2153–2159. IEEE (2012)Google Scholar
  16. 16.
    Guizilini, V., Ramos, F.: Visual odometry learning for unmanned aerial vehicles Robotics and Automation (ICRA), 2011 IEEE International Conference on, pages 6213–6220. IEEE (2011)Google Scholar
  17. 17.
    Holz, D., Basilico, N., Amigoni, F., Behnke, S.: Evaluating the Efficiency of Frontier-Based Exploration Strategies. Munich, Germany (2010)Google Scholar
  18. 18.
    Honegger, D., Meier, L., Tanskanen, P., Marc, P.: An open source and open hardware embedded metric optical flow cmos camera for indoor and outdoor applications IEEE International Conference on Robotics and Automation, Karlsruhe (2013)Google Scholar
  19. 19.
    Kelly, J., Saripalli, S., Sukhatme, G., Laugier, C., Siegwart, R.: Combined visual and inertial navigation for an unmanned aerial vehicle Field and Service Robotics, volume 42 of Springer Tracts in Advanced Robotics, pages 255–264. Springer, Berlin Heidelberg (2008)Google Scholar
  20. 20.
    Koenig, S.: Improved analysis of greedy mapping Proc. of IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, Las Vegas, NV, pages 3251–3257 (2003)Google Scholar
  21. 21.
    Koenig, S., Tovey, C., Halliburton, W.: Greedy mapping of terrain Proc. of IEEE Int. Conf. on Robotics and Automation, volume 4, pages 3594–3599 vol.4 (2001)Google Scholar
  22. 22.
    Krajník, T., Nitsche, M.: A practical multirobot localization system. in review (2013)Google Scholar
  23. 23.
    Krajník, T., Nitsche, M., Faigl, J., Duckett, T., Mejail, M., Preucil, L.: External localization system for mobile robotics Proceedings of the International Conference on Advanced Robotics. IEEE, Montevideo (2013)Google Scholar
  24. 24.
    Krajník, T., Nitsche, M., Pedre, S., Preucil, L., Mejail, M.: A Simple Visual Navigation System for an UAV, p 34. IEEE, Piscataway (2012)Google Scholar
  25. 25.
    Lemaire, T., Berger, C, Jung, Il-K, Lacroix, S.: Vision-based slam: Stereo and monocular approaches. Int. J. Comput. Vis. 74(3), 343–364 (2007)CrossRefGoogle Scholar
  26. 26.
    Makarenko, A. A., Williams, S. B., Bourgault, F., Durrant-Whyte, H. F.: An experiment in integrated exploration. In: In IEEE/RSJ Int. Conf. on Intelligent Robots and System, pages 534–539. IEEE (2002)Google Scholar
  27. 27.
    Newman, P. M., Bosse, M., Leonard, J. J.: Autonomous feature-based exploration. In: IEEE Int. Conf. on Robotics and Automation, Taiwan, Sep, p 2003Google Scholar
  28. 28.
    Rönnbäck, S.: Developement of a ins/gps navigation loop for an uav. Master’s thesis, 81 (2000)Google Scholar
  29. 29.
    Stachniss, C., Grisetti, G., Burgard, W.: Information gain-based exploration using Rao-Blackwellized particle filters. In: Proc. of Robotics: Science and Systems, Cambridge, MA, USA (2005)Google Scholar
  30. 30.
    Wang, C.-L., Wang, T.-M., Liang, J.-H., Zhang, Y.-C., Yi, Z.: Bearing-only visual slam for small unmanned aerial vehicles in gps-denied environments. Int. J. Autom. Comput. 10(5), 387–396 (2013)CrossRefGoogle Scholar
  31. 31.
    Wendel, J., Meister, O., Schlaile, C., Trommer, G. F.: An integrated gps/mems-imu navigation system for an autonomous helicopter. Aerosp. Sci. Technol. 10(6), 527–533 (2006)CrossRefGoogle Scholar
  32. 32.
    Yamauchi, B.: A frontier-based approach for autonomous exploration. In: Proc. of IEEE Int. Symposium on Computational Intelligence in Robotics and Automation, pages 146–151. IEEE Comput. Soc. Press (1997)Google Scholar
  33. 33.
    Zhao, S., Lin, F., Peng, K., Chen, B. M., Lee, T. H.: Homography-based vision-aided inertial navigation of uavs in unknown environments. In: AIAA Guidance, Navigation, and Control Conference (2012)Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Jan Chudoba
    • 1
  • Miroslav Kulich
    • 2
  • Martin Saska
    • 1
  • Tomáš Báča
    • 1
  • Libor Přeučil
    • 2
  1. 1.Department of Cybernetics, Faculty of Electrical EngineeringCzech Technical University in PraguePragueCzech Republic
  2. 2.Czech Institute of Informatics, Robotics, and CyberneticsCzech Technical University in PraguePragueCzech Republic

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