# A Novel Deep Neural Network Architecture for Mars Visual Navigation

arXiv: https://arxiv.org/abs/1808.08395 · HTML: https://mlsanity.com/paper/1808.08395/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2018.

## Built on by (most cited first, 2 of 2)

- [S5Mars: Semi-Supervised Learning for Mars Semantic Segmentation](https://mlsanity.com/paper/2207.01200/) (2022)
- [Efficient Adaptation of Deep Neural Networks for Semantic Segmentation in Space Applications](https://mlsanity.com/paper/2504.15991/) (2025)

## Abstract (arXiv)

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.