Speaker
Description
Use of vision-based AI in critical embedded systems
Artificial Intelligence methods such as Deep Neural Networks are able to provide performances that surpass those of more classical techniques. The potential improvements do not come for free, as the problematique with these techniques lies in the explainability of their results. For this reason, introducing them into critical embedded systems poses some challenges.
In this work, result of an ESA project, we analyze two use cases where Deep Neural Networks are introduced into vision-based navigation systems and implement them on representative space avionics. We developed one model for each use case, the first aiming at detecting and localizing craters in a descent and landing phase over the Moon, and the second aiming at identifying previously selected patches of an unknown satellite on a life visual camera feed.
We present the work developed for each use case, the implementation on avionics representative of space, and the approach followed for verification and validation.
