Reference : Omnidirectional vision applied to Unmanned Aerial Vehicles (UAVs) attitude and headin...
Scientific journals : Article
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/9962
Omnidirectional vision applied to Unmanned Aerial Vehicles (UAVs) attitude and heading estimation
English
Mondragon, Ivan F. [Universidad Politecnica de Madrid (UPM) - Centro de Automatica y Robotica (CAR), Madrid, Spain > Computer Vision Group]
Campoy, Pascual [Universidad Politecnica de Madrid (UPM) - Centro de Automatica y Robotica (CAR), Madrid, Spain > Computer Vision Group]
Martinez, Carol [Universidad Politecnica de Madrid (UPM) - Centro de Automatica y Robotica (CAR), Madrid, Spain > Computer Vision Group]
Olivares Mendez, Miguel Angel mailto [Universidad Politecnica de Madrid (UPM) - Centro de Automatica y Robotica (CAR), Madrid, Spain > Computer Vision Group]
2010
Robotics & Autonomous Systems
Elsevier Science Bv
58
6
SI
809-819
Yes (verified by ORBilu)
International
0921-8890
Amsterdam
[en] Omnidirectional images ; Catadioptric systems ; Unmanned Aerial Vehicles (UAV) ; Sky segmentation
[en] This paper presents an aircraft attitude and heading estimator using catadioptric images as a principal sensor for UAV or as a redundant system for IMU (Inertial Measure Unit) and gyro sensors. First, we explain how the unified theory for central catadioptric cameras is used for attitude and heading estimation, explaining how the skyline is projected on the catadioptric image and how it is segmented and used to calculate the UAV's attitude. Then, we use appearance images to obtain a visual compass, and we calculate the relative rotation and heading of the aerial vehicle. Finally the tests and results using the UAV COLIBRI platform and the validation of them in real flights are presented, comparing the estimated data with the inertial values measured on board. (C) 2010 Elsevier B.V. All rights reserved.
Universidad Politecnica de Madrid ; Consejeria de Educacion de la Comunidad de Madrid ; Fondo Social Europeo (FSE) ; Spanish Science and Technology Ministry [CICYT DPI 2007-66156]
http://hdl.handle.net/10993/9962
10.1016/j.robot.2010.02.012
The work reported in this paper is the conclusion of several research stages at the Computer Vision Group-Universidad Politecnica de Madrid. The authors would like to thank Jorge Leon for supporting the flight trials, the I.A. Institute-CSIC for collaborating in the flights, and the Universidad Politecnica de Madrid, and Consejeria de Educacion de la Comunidad de Madrid and the Fondo Social Europeo (FSE) for the authors' Ph.D. funding. This work has been sponsored by the Spanish Science and Technology Ministry under the Grant CICYT DPI 2007-66156.

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