[en] Remote Sensing using unmanned aerial vehicles (UAV) is gathering a lot of attention at the moment by researchers and developers, especially in terms of low-cost aircrafts which still maintain sufficient accuracy and performance. This paper introduces a low-cost approach to increase airworthiness by using a forward-looking camera to estimate the attitude of a UAV. It not only focuses on using machine learning to classify ground and sky, but also uses image processing and software engineering methods to make it fault-tolerant and really applicable on a miniature UAV. Additionally, it is able to interface with an autopilot framework to being used productively on flight missions.
Chen, Yangquan; University of California, Merced > School of Engineering
VOOS, Holger ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Langue du document :
Anglais
Titre :
Visual Attitude Estimation for Low-Cost Personal Remote Sensing Systems
Date de publication/diffusion :
29 août 2011
Nom de la manifestation :
7th International ASME/IEEE Conference on Mechatronics & Embedded Systems & Applications MESA 2011
Lieu de la manifestation :
Washington, Etats-Unis - District de Columbia
Date de la manifestation :
August 28–31, 2011
Manifestation à portée :
International
Titre de l'ouvrage principal :
7th International ASME/IEEE Conference on Mechatronics & Embedded Systems & Applications MESA 2011, Washington 28-31 August 2011
