This paper presents an algorithm that allows an Unmanned Aerial Vehicle (UAV) to safely fly in GPS-Challenging environments exploiting cooperative and vision-based navigation techniques. In particular, a sensor fusion approach based on the Extended Kalman Filter (EKF) is presented, in which measurements from on-board inertial sensors and magnetometers are integrated with information provided by cooperative UAVs (namely GPS-based position), vision-based tracking, and monocular pose estimation. The performance of the proposed approach has been evaluated on the basis of numerical simulations. Achieved results show the potential of combining both multi-UAV and visionbased navigation approaches where one technique can compensate for the limits of the other, improving the overall navigation performance.

Autonomous Flight in GPS-Challenging Environments Exploiting Multi-UAV Cooperation and Vision-aided Navigation

VETRELLA, AMEDEO RODI;OPROMOLLA, ROBERTO;FASANO, GIANCARMINE;ACCARDO, DOMENICO;GRASSI, MICHELE
2017

Abstract

This paper presents an algorithm that allows an Unmanned Aerial Vehicle (UAV) to safely fly in GPS-Challenging environments exploiting cooperative and vision-based navigation techniques. In particular, a sensor fusion approach based on the Extended Kalman Filter (EKF) is presented, in which measurements from on-board inertial sensors and magnetometers are integrated with information provided by cooperative UAVs (namely GPS-based position), vision-based tracking, and monocular pose estimation. The performance of the proposed approach has been evaluated on the basis of numerical simulations. Achieved results show the potential of combining both multi-UAV and visionbased navigation approaches where one technique can compensate for the limits of the other, improving the overall navigation performance.
978-162410449-7
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/661696
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