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Controlling UAV flights precisely requires a realistic dynamic model andaccurate state estimates from onboard sensors like UAV, GPS and visualobservations. Obtaining a precise dynamic model is extremely difficult, asimportant aerodynamic effects are hard to model, in particular ground effectand other turbulences. While machine learning has been used in the past toestimate UAV created turbulence, this was restricted to flat grounds or diffusein-flight air turbulences, both without taking into account obstacles. In thiswork we address the complex problem of estimating in-flight turbulences causedby obstacles, in particular the complex structures in cluttered environments.We learn a mapping from control input and images captured by onboard cameras toturbulence. In a large-scale setting, we train a model over a large number ofdifferent simulated photo-realistic environments loaded into the Habitat.AIsimulator augmented with a dynamic UAV model and an analytic ground effectmodel. We transfer the model from simulation to a real environment and evaluateon real UAV flights from the EuRoC-MAV dataset, showing that the model iscapable of good sim2real generalization performance. The dataset will be madepublicly available upon acceptance.

Learning to estimate UAV created turbulence from scene structure observed by onboard cameras