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Visual servoing applied to real-time stabilization of a multi-rotor UAV

Published online by Cambridge University Press:  13 February 2012

Hugo Romero,
Sergio Salazar and
Rogelio Lozano
Hugo Romero*
Affiliation:
CITIS-UAEH, Area Académica de Computación, 42184 Pachuca Hidalgo, MéxicoUMI CNRS CINVESTAV, Av. IPN 2508 Col. San Pedro Zacatenco 07360 México D.F., México
Sergio Salazar
Affiliation:
UMI CNRS CINVESTAV, Av. IPN 2508 Col. San Pedro Zacatenco 07360 México D.F., México
Rogelio Lozano
Affiliation:
UMI CNRS CINVESTAV, Av. IPN 2508 Col. San Pedro Zacatenco 07360 México D.F., MéxicoUTC, Heudiasyc Centre de Recherches Royallieu BP 20529 60205 Compiègne Cedex France
*
*Corresponding author. E-mail: hromero72@gmail.com
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Summary

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In this paper we address the problem of stabilization and local positioning of a four-rotor rotorcraft using computer vision. Our approaches to estimate the orientation and position of the rotorcraft combine the measurements from an Inertial Measurement Unit (IMU) and a vision system composed of a single camera. In the first stage, the vision system is used to estimate the position and yaw angle of the rotorcraft, while in the second stage the vision system is used to estimate the translational velocity of the flying robot. In both cases the IMU gives the pitch and roll angles at a higher rate. The technique used to estimate the position of the rotorcraft in the first stage combines the homogeneous transformation approach for the camera calibration process with the plane-based pose method for estimating the position. In the second stage, a navigation system using the optical flow is also developed to estimate the translational velocity of the aircraft. We present real-time experiments of stabilization and location of a four-rotor rotorcraft.

Keywords

Visual servoingNavigation systemUAVs

Information

Type
Articles
Information
Robotica , Volume 30 , Issue 7 , December 2012, pp. 1203 - 1212
Copyright
Copyright © Cambridge University Press 2012

References

1. Oertel, C. H., “Machine vision-based sensing for helicopter flight control,” Robotica 18 (3), 299–303 (2000).CrossRefGoogle Scholar
2. Shin, J., Nonami, K., Fujiwara, D. and Hazawa, K., “Model-based optimal attitude and positioning control of small-scale unmanned helicopter,” Robotica 23 (1), 51–63 (2005).CrossRefGoogle Scholar
3. Wu, A., Johnson, E. and Proctor, A., “Vision-Aided Inertial Navigation for Flight Control,” Proceedings of the AIAA Guidance, Navigation, and Control Conference and Exhibit, San Francisco, CA (Aug. 15–18, 2005).Google Scholar
4. Mejias, L., Saripalli, S., Campoy, P. and Sukhatme, S., “Visual servoing of an autonomous helicopter in Urban areas using feature tracking,” J. Field Robot. 23 (3–4), 185–199 (2006).CrossRefGoogle Scholar
5. Yoshihata, Y., Watanabe, K., Iwatini, Y. and Hashimoto, K., “Multi-Camera Visual Servoing of a Micro Helicopter Under Occlusions,” In: Proceedings of IEEE International Conference on Intelligent Robots and Systems 2007, pp. 2615–2620.Google Scholar
6. Saripalli, S., Montgomery, J. F. and Sukhatme, G. S., “Visually-guided landing of an unmaned aerial vahicle,” IEEE Trans Robot. Autom. 19 (3), 371–381 (2003).CrossRefGoogle Scholar
7. Nordberg, K., Doherty, P., Farnebäck, G., Forssén, P. E., Granlund, G., Moe, A. and Wiklund, J., “Vision for a UAV Helicopter,” Proceedings of the International Conference on Intelligent Robots and Systems (IROS) 2002, Lausanne, Switzerland.Google Scholar
8. Altug, E., Ostrowski, J. P. and Taylor, C. J., “Control of a quadrotor helicopter using dual camera visual feedback,” Int. J. Robot. Res. 24 (5), 329–341 (May 2005).CrossRefGoogle Scholar
9. Bourquardez, O., Mahony, R., Guenard, N., Chaumette, F., Hamel, T. and Eck, L., “Image-based visual servo control of the translation kinematics of a quadrotor aerial vehicle,” IEEE Trans. Robot. 25 (3), 743–749 (June 2009).CrossRefGoogle Scholar
10. Herisse, B., Russotto, F., Hamel, T. and Mahony, R., “Hovering Flight and Vertical Landing Control of a VTOL Unmanned Aerial Vehicle using Optical Flow, Proceedings of the 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, Nice, France (Sep. 22–26, 2008).Google Scholar
11. Amidi, O., Kanade, T. and Fujita, K., “A visual odometer for autonomous helicopter flight,” J. Robot. Auton. 28, 185–193 (1999).CrossRefGoogle Scholar
12. Corke, P., “An inertial and visual sensing system for a small autonomous helicopter,” J. Robot. Syst. 18, 43–51 (2004).CrossRefGoogle Scholar
13. Corke, P., Strelow, D. and Singh, S., Omnidirectional Visual Odometry for a Planetary Rover, Proceedings of IEEE International Conference on Intelligent Robots and Systems (IROS) 2004.Google Scholar
14. Hartley, R. and Zisserman, A., Multiple View Geometry in Computer Vision, 2nd ed. (Cambridge University Press, Cambridge, UK, 2004). ISBN 0521540518.CrossRefGoogle Scholar
15. Corke, P. I., Visual Control of Robots, High-Performance Visual Servoing, 1st ed. (John Wiley, Hoboken, NJ, 1996). ISBN 0471969370.Google Scholar
16. Romero, H., Modelisation et Asservissement Visuel d'un Mini-hèlicoptére Ph.D. Thesis in French (Universié de Technologie de Compiègne France, Julio 2008).Google Scholar
17. Viola, P. and Jones, M., “Rapid Object Detection Using a Boosted Cascade of Simple Features,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Patter Recognition. Vol. 1, 511–518 (2001).Google Scholar
18. Sturm, P., “Algorithms for plane-based pose estimation,” In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2000), June 13–15, Hilton Head, SC, pp. 706–711.Google Scholar
19. Beauchemin, S. S. and Barron, J. L., “The computation of optical flow,” ACM Comput. Surv. 27, 433–467 (1995).CrossRefGoogle Scholar
20. Bouguet, J. Y., “Pyramidal Implementation of the Lucas Kanade Feature Tracker,” Technical report, Intel Corporation, Santa Clara, CA (1999).Google Scholar
21. Green, W. E., Oh, P. Y. and Barrows, G. L., “Flying Insect Inspired Vision for Autonomous Aerial Robot Maneuvers in Near-Earth Environments,” Proceedings of IEEE International Conference on Robotics and Automation, ICRA (2004).CrossRefGoogle Scholar
22. Castillo, P., Dzul, A. and Lozano, R., “Real-time stabilization and tracking of a four rotor mini rotorcraft,” IEEE Trans. Control Syst. Technol. 12 (4) 510–516 (2004).CrossRefGoogle Scholar
23. Tell, A. R., “Global stabilization and restricted tracking for multiple integrator with bounded controls,” Syst. Control Lett. 18, 165–171 (1992).CrossRefGoogle Scholar
24. Salazar-Cruz, S., Escareño, J., Lara, D. and Lozano, R., “Embedded control system for a four-rotor UAV,” Int. J. Adapt. Control Signal Process. 21 (2–3), 189–204 (2007).CrossRefGoogle Scholar
25. Merhav, S., Aerospace Sensor Systems and Applications (Springer-Verlag, New York, 1996). ISBN 0387946055.CrossRefGoogle Scholar
26. Romero, H., Salazar, S. and Lozano, R., “Real time stabilization of an eight-rotor UAV using optical flow,” IEEE Trans. Robot. 25 (4), 809–817 (Aug. 2009).CrossRefGoogle Scholar