Skip to main content

Advertisement

Springer Nature Link
Log in

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

  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Vicon motion systems: http://www.vicon.com (2014)

  2. Amidi, O.: An Autonomous Vision-Guided Helicopter. PhD thesis, Carnegie Mellon University, Department of Electrical and Computer Engineering Pittsburgh, PA 15213 (1996)

  3. Francesco, A., Vincenzo, C.: An information-based exploration strategy for environment mapping with mobile robots. Robot. Auton. Syst. 58(5), 684–699 (2010)

    Article  Google Scholar 

  4. Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: SURF: Speeded up robust features. Comput. Vis. Image Underst. 110(3), 346–359 (2008)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  10. Conte, G., Doherty, P.: An integrated uav navigation system based on aerial image matching. IEEE (2008)

  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)

    MATH  Google Scholar 

  12. Dijkstra, E. W.: A note on two problems in connexion with graphs. Numer. Math. 1(1), 269–271 (1959)

    Article  MathSciNet  MATH  Google Scholar 

  13. Fowers, S.G.: Stabilization and control of a quad-rotor micro-uav using vision sensors (2008)

  14. Gonzalez-Banos, H. H., Latombe, J.-C.: Navigation strategies for exploring indoor environments. Int. J. Robot. Res. 21(10-11), 829–848 (2002)

    Article  Google Scholar 

  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)

  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)

  17. Holz, D., Basilico, N., Amigoni, F., Behnke, S.: Evaluating the Efficiency of Frontier-Based Exploration Strategies. Munich, Germany (2010)

    Google Scholar 

  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)

  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. 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)

  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)

  22. Krajník, T., Nitsche, M.: A practical multirobot localization system. in review (2013)

  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. 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. 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)

    Article  Google Scholar 

  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)

  27. Newman, P. M., Bosse, M., Leonard, J. J.: Autonomous feature-based exploration. In: IEEE Int. Conf. on Robotics and Automation, Taiwan, Sep, p 2003

  28. Rönnbäck, S.: Developement of a ins/gps navigation loop for an uav. Master’s thesis, 81 (2000)

  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)

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

  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)

Download references

Author information

Authors and Affiliations

  1. Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic

    Jan Chudoba, Martin Saska & Tomáš Báča

  2. Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University in Prague, Prague, Czech Republic

    Miroslav Kulich & Libor Přeučil

Authors
  1. Jan Chudoba
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Miroslav Kulich
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Martin Saska
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Tomáš Báča
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Libor Přeučil
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Martin Saska.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chudoba, J., Kulich, M., Saska, M. et al. Exploration and Mapping Technique Suited for Visual-features Based Localization of MAVs. J Intell Robot Syst 84, 351–369 (2016). https://doi.org/10.1007/s10846-016-0358-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10846-016-0358-8

Keywords