  Disturbance estimation for Micro Aerial Vehicles (MAVs) is crucial for
robustness and safety. In this paper, we use novel, bio-inspired airflow
sensors to measure the airflow acting on a MAV, and we fuse this information in
an Unscented Kalman Filter (UKF) to simultaneously estimate the
three-dimensional wind vector, the drag force, and other interaction forces
(e.g. due to collisions, interaction with a human) acting on the robot. To this
end, we present and compare a fully model-based and a deep learning-based
strategy. The model-based approach considers the MAV and airflow sensor
dynamics and its interaction with the wind, while the deep learning-based
strategy uses a Long Short-Term Memory (LSTM) neural network to obtain an
estimate of the relative airflow, which is then fused in the proposed filter.
We validate our methods in hardware experiments, showing that we can accurately
estimate relative airflow of up to 4 m/s, and we can differentiate drag and
interaction force.
