  Terrain traversability analysis plays a major role in ensuring safe robotic
navigation in unstructured environments. However, real-time constraints
frequently limit the accuracy of online tests especially in scenarios where
realistic robot-terrain interactions are complex to model. In this context, we
propose a deep learning framework trained in an end-to-end fashion from
elevation maps and trajectories to estimate the occurrence of failure events.
The network is first trained and tested in simulation over synthetic maps
generated by the OpenSimplex algorithm. The prediction performance of the Deep
Learning framework is illustrated by being able to retain over 94% recall of
the original simulator at 30% of the computational time. Finally, the network
is transferred and tested on real elevation maps collected by the SEEKER
consortium during the Martian rover test trial in the Atacama desert in Chile.
We show that transferring and fine-tuning of an application-independent
pre-trained model retains better performance than training uniquely on scarcely
available real data.
