SafeUAV: Learning to estimate depth and safe landing areas for UAVs from synthetic data

The emergence of relatively low cost UAVs has prompted a global concern about the safe operation of such devices. Since most of them can ‘autonomously’ fly by means of GPS way-points, the lack of a higher logic for emergency scenarios leads to an abundance of incidents involving property or personal injury. In order to tackle this problem, we propose a small, embeddable ConvNet for both depth and safe landing area estimation. Furthermore, since labeled training data in the 3D aerial field is scarce and ground images are unsuitable, we capture a novel synthetic aerial 3D dataset obtained from 3D reconstructions. We use the synthetic data to learn to estimate depth from in-flight images and segment them into ‘safe-landing’ and ‘obstacle’ regions. Our experiments demonstrate compelling results in practice on both synthetic data and real RGB drone footage.

@inproceedings{marcu2018safeuav,
  title = {SafeUAV: Learning to estimate depth and safe landing areas for UAVs from synthetic data},
  author = {Alina Marcu and Dragos Costea and Vlad Licaret and Mihai Pirvu and Emil Slusanschi and Marius Leordeanu},
  year = {2018},
  booktitle = {Proceedings of the European Conference on Computer Vision Workshops (ECCVW)},
}