no code implementations • 12 Sep 2018 • Clément Pinard, Laure Chevalley, Antoine Manzanera, David Filliat
We propose a depth map inference system from monocular videos based on a novel dataset for navigation that mimics aerial footage from gimbal stabilized monocular camera in rigid scenes.
no code implementations • 12 Sep 2018 • Clément Pinard, Laure Chevalley, Antoine Manzanera, David Filliat
We then present results on a synthetic dataset that we believe to be more representative of typical UAV scenes.
no code implementations • 12 Sep 2018 • Clément Pinard, Laure Chevalley, Antoine Manzanera, David Filliat
Using a neural network architecture for depth map inference from monocular stabilized videos with application to UAV videos in rigid scenes, we propose a multi-range architecture for unconstrained UAV flight, leveraging flight data from sensors to make accurate depth maps for uncluttered outdoor environment.