Trinocular ground system to control UAVs

In this paper we introduce a real-time trinocular system to control rotary wing Unmanned Aerial Vehicles based on the 3D information extracted by cameras located on the ground. The algorithm is based on key features onboard the UAV to estimate the vehicle's position and orientation. The algorithm is validated against onboard sensors and known 3D positions, showing that the proposed camera configuration robustly estimates the helicopter's position with an adequate resolution, improving the position estimation, especially the height estimation. The obtained results show that the proposed algorithm is suitable to complement or replace the GPS-based position estimation in situations where GPS information is unavailable or where its information is inaccurate, allowing the vehicle to develop tasks at low heights, such as autonomous landing, take-off, and positioning, using the extracted 3D information as a visual feedback to the flight controller.

[1]  Timothy W. McLain,et al.  Performance Evaluation of Vision-Based Navigation and Landing on a Rotorcraft Unmanned Aerial Vehicle , 2007, 2007 IEEE Workshop on Applications of Computer Vision (WACV '07).

[2]  Luis Mejias Alvarez Control visual de un vehículo aéreo autónomo usando detección y seguimiento de características en espacios exteriores , 2011 .

[3]  Peter I. Corke,et al.  A tutorial on visual servo control , 1996, IEEE Trans. Robotics Autom..

[4]  Eric N. Johnson,et al.  Vision-Aided Inertial Navigation for Flight Control , 2005, J. Aerosp. Comput. Inf. Commun..

[5]  Larry D. Hostetler,et al.  The estimation of the gradient of a density function, with applications in pattern recognition , 1975, IEEE Trans. Inf. Theory.

[6]  Glenn P. Tournier,et al.  Estimation and Control of a Quadrotor Vehicle Using Monocular Vision and Moire Patterns , 2006 .

[7]  Emanuele Trucco,et al.  Introductory techniques for 3-D computer vision , 1998 .

[8]  Gaurav S. Sukhatme,et al.  A visual servoing approach for tracking features in urban areas using an autonomous helicopter , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[9]  Michael J. Swain,et al.  Color indexing , 1991, International Journal of Computer Vision.

[10]  Gary Bradski,et al.  Computer Vision Face Tracking For Use in a Perceptual User Interface , 1998 .

[11]  Gaurav S. Sukhatme,et al.  Detection and Tracking of External Features in an Urban Environment Using an Autonomous Helicopter , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[12]  Jean-Yves Bouguet,et al.  Camera calibration toolbox for matlab , 2001 .

[13]  Ming Liu,et al.  Stereo Vision based Relative Pose and Motion Estimation for Unmanned Helicopter Landing , 2006, 2006 IEEE International Conference on Information Acquisition.

[14]  Peter Corke An Inertial and Visual Sensing System for a Small Autonomous Helicopter , 2004, J. Field Robotics.

[15]  Takeo Kanade,et al.  A visual odometer for autonomous helicopter flight , 1999, Robotics Auton. Syst..

[16]  Dorin Comaniciu,et al.  Robust analysis of feature spaces: color image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[17]  Camillo J. Taylor,et al.  Control of a Quadrotor Helicopter Using Dual Camera Visual Feedback , 2005, Int. J. Robotics Res..

[18]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Raja Sengupta,et al.  Obstacle Detection for Small Autonomous Aircraft Using Sky Segmentation , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[20]  Gaurav S. Sukhatme,et al.  Visually guided landing of an unmanned aerial vehicle , 2003, IEEE Trans. Robotics Autom..

[21]  Gaurav S. Sukhatme,et al.  Combined optic-flow and stereo-based navigation of urban canyons for a UAV , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[22]  Miguel A. Olivares-Méndez,et al.  Computer Vision Onboard UAVs for Civilian Tasks , 2009, J. Intell. Robotic Syst..