This paper presents an algorithm for cooperative UAV navigation that exploits vision-based sensing, standalone GPS, differential GPS among antennas embarked on different flying platforms, and measurements obtained by inertial sensors and magnetometers. Unlike other cooperative navigation approaches, the developed technique is mainly aimed at improving navigation performance in outdoor environments, either in real time or off-line. The logical architecture and the main processing steps are discussed. Then, algorithms for differential GPS/vision processing and sensor fusion for navigation state estimation are introduced. Covariance analysis is used for theoretical performance assessment. The hardware system used for concept demonstration comprises a customized quadrotor and different GPS antennas and receivers, and is briefly described. Finally, first results from experimental tests are presented. In particular, the attitude solution obtained by differential GPS and vision is compared with the estimates provided by the onboard autopilot system.
Cooperative UAV navigation based on distributed multi-antenna GNSS, vision, and MEMS sensors / Vetrella, AMEDEO RODI; Fasano, Giancarmine; Renga, Alfredo; Accardo, Domenico. - (2015), pp. 1128-1137. (Intervento presentato al convegno 2015 International Conference on Unmanned Aircraft Systems, ICUAS 2015 tenutosi a Denver Marriott Tech Center, usa nel 2015) [10.1109/ICUAS.2015.7152405].
Cooperative UAV navigation based on distributed multi-antenna GNSS, vision, and MEMS sensors
VETRELLA, AMEDEO RODI;FASANO, GIANCARMINE;RENGA, ALFREDO;ACCARDO, DOMENICO
2015
Abstract
This paper presents an algorithm for cooperative UAV navigation that exploits vision-based sensing, standalone GPS, differential GPS among antennas embarked on different flying platforms, and measurements obtained by inertial sensors and magnetometers. Unlike other cooperative navigation approaches, the developed technique is mainly aimed at improving navigation performance in outdoor environments, either in real time or off-line. The logical architecture and the main processing steps are discussed. Then, algorithms for differential GPS/vision processing and sensor fusion for navigation state estimation are introduced. Covariance analysis is used for theoretical performance assessment. The hardware system used for concept demonstration comprises a customized quadrotor and different GPS antennas and receivers, and is briefly described. Finally, first results from experimental tests are presented. In particular, the attitude solution obtained by differential GPS and vision is compared with the estimates provided by the onboard autopilot system.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
