A Few-Shot Learning Approach for Sim2Real Martian Imagery Classification

Autonomous navigation systems for Mars rovers face significant challenges due to the limited availability of real Martian imagery for training Artificial Intelligence (AI) models. In contrast, a large amount of synthetic images of Martian landscape can be generated and used for training. This paper presents a novel”Sim2Real” approach that integrates synthetic data generation and classification with few-shot learning to enhance real images classification for autonomous Mars rovers. Specifically, a custom Convolutional Neural Network (CNN) is first developed and pre-trained on synthetic Martian landscapes provided by Thales Alenia Space Italy (TAS-I). The pre-trained CNN is then used as a basis to develop the proposed Sim2Real approach. In particular, a few-shot learning strategy is introduced to classify real Mars rover images from an existing available dataset of the National Aeronautics and Space Administration (NASA). Preliminary experimental results demonstrated promising performance when applied to real Martian images, highlighting the potential of few-shot based Sim2Real approaches for planetary exploration.