Speaker
Description
Artificial Intelligence methods such as Deep Neural Networks can provide performances that surpass those of more classical techniques. The potential improvements do not come for free, as the difficulties 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 analyse 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.
