  In this paper, emerging deep learning techniques are leveraged to deal with
Mars visual navigation problem. Specifically, to achieve precise landing and
autonomous navigation, a novel deep neural network architecture with double
branches and non-recurrent structure is designed, which can represent both
global and local deep features of Martian environment images effectively. By
employing this architecture, Mars rover can determine the optimal navigation
policy to the target point directly from original Martian environment images.
Moreover, compared with the existing state-of-the-art algorithm, the training
time is reduced by 45.8%. Finally, experiment results demonstrate that the
proposed deep neural network architecture achieves better performance and
faster convergence than the existing ones and generalizes well to unknown
environment.
