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Proceedings Paper

Fusion of inertial, optical flow, and airspeed measurements for UAV navigation in GPS-denied environments
Author(s): Andrey Soloviev; Adam J. Rutkowski
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Paper Abstract

This paper describes the data fusion approach that is developed for navigation of autonomous unmanned aerial vehicles (UAVs) for those applications where the Global Positioning System (GPS) signals are denied. Example scenarios include navigation under interference and jamming and urban navigation missions. The system architecture is biologically inspired and exploits measurements that are utilized by flying insects for self-localization purposes. The data fusion algorithm implements the Kalman filter mechanization that fuses INS data (position velocity and attitude), optical flow data from a monocular downward looking visual system (scaled body-frame vehicle velocity components), and compass measurements (azimuth angle). Kalman filter measurement observables are formulated in a complimentary form, i.e., as differences between optical flow/compass measurements and INS states that are projected into the measurement domain. The filter estimates inertial error states and error in the flight height. We present the navigation solution architecture and demonstrate its feasibility using simulations and actual data experiments. Also, we compare our results to a data fusion algorithm that fuses airspeed and optical flow measurements.

Paper Details

Date Published: 30 April 2009
PDF: 12 pages
Proc. SPIE 7332, Unmanned Systems Technology XI, 733202 (30 April 2009); doi: 10.1117/12.820177
Show Author Affiliations
Andrey Soloviev, Univ. of Florida (United States)
Adam J. Rutkowski, Air Force Research Lab. (United States)

Published in SPIE Proceedings Vol. 7332:
Unmanned Systems Technology XI
Grant R. Gerhart; Douglas W. Gage; Charles M. Shoemaker, Editor(s)

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