Mid-Air: A multi-modal dataset for extremely low altitude drone flights

Flying a drone in unstructured environments with varying conditions is challenging. To help producing better algorithms, we present Mid-Air, a multi-purpose synthetic dataset for low altitude drone flights in unstructured environments. It contains synchronized data of multiple sensors for a total of 54 trajectories and more than 420k video frames simulated in various climate conditions. In this work, we motivate design choices, explain how the data was simulated, and present the content of the dataset. Finally, a benchmark for positioning and a benchmark for image generation tasks show how Mid-Air can be used to set up a standard evaluation method for assessing computer vision algorithms in terms of robustness and generalization. We illustrate this by providing a baseline for depth estimation and by comparing it with results obtained on an existing dataset. The Mid-Air is publicly downloadable, with additional details on the data format and organization, at http://midair.ulg.ac.be