A deep learning approach for optical autonomous planetary relative terrain navigation

Autonomous relative terrain navigation is a problem at the forefront of many space missions involving close proximity operations which does not have any definitive answer. There are many techniques to help cope with this issue using both passive and active sensors, but almost all require very sophisticated dynamical models. Convolutional Neural Networks (CNNs) trained with images rendered from a digital terrain map (DTM) can provide a way to side-step the issue of unknown or complex dynamics while still providing reliable autonomous navigation by directly mapping an image to position. The portability of trained CNNs allows offline training that can yield a matured network capable of being loaded onto a spacecraft for real-time position acquisition.