Vision-based unmanned aerial vehicle navigation using geo-referenced information

Authors:
Gianpaolo Conte;Patrick Doherty
Affiliations:
Artificial Intelligence and Integrated Computer System Division, Department of Computer and Information Science, Linköping University, Linköping, Sweden;Artificial Intelligence and Integrated Computer System Division, Department of Computer and Information Science, Linköping University, Linköping, Sweden
Venue:
EURASIP Journal on Advances in Signal Processing - Special issue on signal processing advances in robots and autonomy
Year:
2009

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Abstract

This paper investigates the possibility of augmenting an Unmanned Aerial Vehicle (UAV) navigation system with a passive video camera in order to cope with long-term GPS outages. The paper proposes a vision-based navigation architecture which combines inertial sensors, visual odometry, and registration of the on-board video to a geo-referenced aerial image. The vision-aided navigation system developed is capable of providing high-rate and drift-free state estimation for UAV autonomous navigation without the GPS system. Due to the use of image-to-map registration for absolute position calculation, drift-free position performance depends on the structural characteristics of the terrain. Experimental evaluation of the approach based on offline flight data is provided. In addition the architecture proposed has been implemented on-board an experimental UAV helicopter platform and tested during vision-based autonomous flights.