Possible landing support variants while unmanned multi-rotor flying vehicle's off-line control


Aksenov A. Y.*, Zaytseva A. A.**, Kuleshov S. V.***, Nenausnikov K. V.****

Saint-Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, 39, 14th line, Saint-Petersburg, 199178, Russia

*e-mail: a_aksenov@mail.iias.spb.su
**e-mail: cher@iias.spb.su
***e-mail: kuleshov@iias.spb.su
****e-mail: konstantin2113@mail.ru


The paper analyses the problem of a small multi-rotor UAV control in various flight modes. The modern tendencies of control systems design for self-supporting UAVs assume employing methods allowing decrease the level of the spacial position uncertainty and surrounding conditions of the UAV itself without external pilot’s (operator) engagement.

With this, the standard technological solution for a certain number of critical flight phases, requiring precise spatial positioning and control (such as take-off, hovering over landing site, and landing) does not exist. These phases require more precise UAV’s control, due to the necessity of holding it in a specified point in space. The presented article analyses principles of auto-take-off-auto-landing systems design, based on computer vision (CV) methods implementation, and considers specifics of markers employing.

The article analyses the existing auto-take-off-auto-landing systems without global positioning implementation. It suggests an approach to the auto-take-off-auto-landing system design based on computer vision, for an UAV of a multi rotor or a helicopter type. Three types of the system components mutual arrangement were considered and analyzed, and merits and demerits of each configuration were formulated.

The article shows that with any cameras positioning within the auto-take-off-auto-landing system, it is necessary to employ markers to facilitate localization of the key points the UAV or a landing site by the CV systems. Various methods of markers’ positioning and realization of marker labels, depending on marker’s position were analyzed. Markers on the landing site can be realized by the following methods: contrast image of some pattern, liable to identification by the CV methods; lights, remotely controlled by the processor; colored or infrared LEDs, operating in continuous or pulse mode. Variants of marker labels on an UAV are as follows: colored or infrared LEDs, operating in continuous or pulse mode; lights, remotely controlled by the processor; the system of angle reflectors and illuminator; contrast image-pattern (including a fuselage shape).

The studies revealed that the controlled markers mode was preferable in all cases, when formation of a control channel between an UAV and landing system was possible.

Further research will be devoted to development of software for auto-landing system based on obtained marker implementation recommendations.


unmanned aerial vehicles, UAV, multi copters, markers, computer vision, CV, auto-landing


  1. Aksenov A.Y., Kuleshov S.V., Zaytseva A.A. An application of computer vision systems to solve the problem of unmanned aerial vehicle control. J. Transport and Telecommunication, 2014, vol. 15, no. 3, pp. 209-214

  2. Altug E., Ostrowski J.P., Taylor C.J. Control of a quadrotor helicopter using dual camera visual feedback, Int. J. Rob. Res. 2005, no. 24 (5), pp. 329–341.

  3. Barbasov V.K., Gavryushin N.M. Dryga D.O., Vataev M.S., Altynov A.E. Inzhenernye izyskaniya, 2012, no. 10. pp. 38-42.

  4. Barbasov V.K., Grechishchev A.V. Inzhenernye izyskaniya, 2014, no. 8, pp. 27-31.

  5. DJI Innovations. “Naza for Multi-Rotor User Manual. Guangdong”. (V 2.8 2013.05.03 Revision), 2013.

  6. Saripalli, S., Montgomery, J.F., Sukhatme, G.S. Visually guided landing of an unmanned aerial vehicle, IEEE Transactions on Robotics and Automation, 2003, vol. 19, no. 3, pp. 371-380.

  7. Radu Horaud, Miles Hansard, Georgios Evangelidis, Menier Cl ́ement. An Overview of Depth Cameras and Range Scanners Based on Time-of-Flight Technologies. Machine Vision and Applications Journal, 2016, available at: https://hal.inria.fr/hal-01325045

  8. Lidar: range-resolved optical remote sensing of the atmosphere series, Springer series in optical sciences, vol. 102 / C. Weitkamp (Ed.), New York, Springer, 2005, 460 p.

  9. Avtomaticheskaya posadka BPLA naryadnyy avtomobil,’ available at: http://absrf.ru/ru/technology/2016-01-26.htm (accessed 05.09.2017).

  10. Veremeenko K.K., Pronkin A.N., Repnikov A.V. Trudy MAI, 2011, no. 49, available at: http://trudymai.ru/eng/published.php?ID=28110

  11. Pavlova N.V., Smejuha A.V. Trudy MAI, 2016, no. 87, available at: http://trudymai.ru/eng/published.php?ID=69703

  12. Corke P. An inertial and visual sensing system for a small autonomous helicopter // Journal of robotic systems, 2004, vol. 21, no. 2, pp. 43–51.

  13. Cesetti A., Frontoni E., Mancini A. et al. A Visual Global Positioning System for Unmanned Aerial Vehicles Used in Photogrammetric Applications. J. Intell Robot Syst, 2011, no. 61, p. 157. doi:10.1007/s10846-010-9489-5

  14. Garcia Carrillo, L.R., Dzul Lopez, A.E., Lozano, R. et al. Combining Stereo Vision and Inertial Navigation System for a Quad-Rotor UAV, J. Intell. Robot. Syst, 2012, no. 65, pp. 373. doi:10.1007/s10846-011-9571-7

  15. Cesetti A., Frontoni E., Mancini A., Zingaretti P., Longhi S. A Vision-Based Guidance System for UAV Navigation and Safe Landing using Natural Landmarks, Sel ected papers fr om the 2nd International Symposium on UAVs, 2008, Reno, Nevada, U.S.A. June 8–10, pp. 233-257.

  16. Levin A., Szeliski R. Visual odometry and map correlation. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, DC, USA. 2004

  17. ETH IDSC. “Flying Machine Arena. Zurich”. available at: http://www.idsc.ethz.ch, 2014.

  18. Ritz, R., Müller, M.W., Hehn, M., D’Andrea, R. Cooperative quadrocopter ball throwing and catching. Proceedings of Intelligent Robots and Systems (IROS). IEEE/RSJ International Conference. Vilamoura. October 2012, IEEE, pp. 4972-4978.

  19. Alexey Y. Aksenov, Sergey V. Kuleshov, Alexandra A. Zaytseva. An Application of Computer Vision Systems to Unmanned Aerial Vehicle Autolanding. A. Ronzhin et al. (Eds.), ICR 2017, LNAI 10459, 2017, pp. 105–112. DOI: 10.1007/978-3-319-66471-2_12

  20. Open Computer Vision, available at: http://sourceforge.net/projects/opencvlibrary/, 2017.

  21. Kuleshov S.V., Zaitseva A.A. Informatsionno-izmeritel’nye i upravlyayushchie sistemy, 2008, vol. 6, no. 10, pp. 88-90.

  22. Kuleshov S.V., Zaitseva A.A. Trudy SPIIRAN, 2008, no. 7, pp. 41-47.

  23. Kuleshov S.V., Yusupov R.M. Trudy SPIIRAN, 2016, no. 46, pp. 5-13, doi:10.15622/sp.46.1


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