Learning to predict obstacle aerodynamics from depth images for Micro Air Vehicles

Many applications of Micro Air Vehicles (MAVs) require them to operate in cluttered environments, flying in constrained spaces and close to obstacles. Such obstacles affect the airflow around the MAV and can thereby affect its flight characteristics. We describe a system for predicting these effects at a distance, using depth images obtained from an RGB-D sensor. Predictions are based on learning from prior experience gathered during training flights. We show that aerodynamic effects caused by obstacles are consistent, and demonstrate that it is practical to make predictions from experience without running a computationally expensive aerodynamic simulation. Our approach uses a Gaussian process regression, it requires minimal parameter tuning and is able to predict the acceleration that will be expected at a distance in the future. The method produces estimates within 12ms without any code optimisation and the results indicate good prediction ability with mean errors within 4-10cm/s² on a database of various obstacles.