  Controlling UAV flights precisely requires a realistic dynamic model and
accurate state estimates from onboard sensors like UAV, GPS and visual
observations. Obtaining a precise dynamic model is extremely difficult, as
important aerodynamic effects are hard to model, in particular ground effect
and other turbulences. While machine learning has been used in the past to
estimate UAV created turbulence, this was restricted to flat grounds or diffuse
in-flight air turbulences, both without taking into account obstacles. In this
work we address the complex problem of estimating in-flight turbulences caused
by obstacles, in particular the complex structures in cluttered environments.
We learn a mapping from control input and images captured by onboard cameras to
turbulence. In a large-scale setting, we train a model over a large number of
different simulated photo-realistic environments loaded into the Habitat.AI
simulator augmented with a dynamic UAV model and an analytic ground effect
model. We transfer the model from simulation to a real environment and evaluate
on real UAV flights from the EuRoC-MAV dataset, showing that the model is
capable of good sim2real generalization performance. The dataset will be made
publicly available upon acceptance.
