Vision-based Gyroscope Fault Detection for UAVs
Résumé
This paper presents a vision-based fault detection
and isolation architecture for unmanned aerial vehicles. The
vehicle’s attitude is computed from visual input through a horizon
tracking algorithm, independently of any other sensor. In a
second stage, two Kalman filters are used for fault detection and
identification in two gyroscopes. The loosely coupled architecture
is suitable for real-time application. The algorithm was implemented
with the ROS framework and the system’s performance
is evaluated in a real-time application scenario with artificially
introduced sensor faults.
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