A Novel Deep Neural Network Architecture for Mars Visual Navigation
This addresses autonomous navigation for Mars rovers, representing an incremental improvement with specific gains in efficiency.
The paper tackles Mars visual navigation by designing a novel deep neural network architecture with double branches and non-recurrent structure to determine optimal navigation policies from Martian images, reducing training time by 45.8% compared to existing methods.
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.