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We propose the use of basic inertial measurement units (IMU) which containsensors such as accelerometers and gyroscopes already on-board drones to detectthe speed and direction of wind gusts. The ability to quickly sense wind gustshas many applications, the most notable of which is in flight assistance of thedrone, where it may adjust motor power parameter to compensate for suchexternal factors or steer the drone toward a safer direction. To illustrate thefeasibility of the approach, we conducted studies to assess how reliably windspeed and wind direction can be detected while a quad-copter drone is hoveringand then in-motion, using off-the-shelf classifiers. Empirical results withreal-life data, collected on a micro aerial vehicle (MAV) in a physical roomwith a consumer-grade fan, show that (1.1) wind speed can be detected with highaccuracy after training, not only on the same drone, but also across differentdrones of the same class, while (1.2) wind direction can be detected with highaccuracy after training on the same drone, but with limited generalizability toother drones. (1.3) We demonstrate how real-time detection of wind speed, usingoffline trained models, is feasible and can be done with high accuracy. (2.1)Finally, we find the reason behind the lower accuracy for wind directiondetection during the analysis of drones in-motion.

Wind Gust Detection using Physical Sensors in Quadcopters