This article addresses the challenge of vision-based proximity navigation in
asteroid exploration missions and on-orbit servicing. Traditional feature
extraction methods struggle with the significant appearance variations of
asteroids due to limited scattered light. To overcome this, we propose a
lightweight feature extractor specifically tailored for asteroid proximity
navigation, designed to be robust to illumination changes and affine
transformations. We compare and evaluate state-of-the-art feature extraction
networks and three lightweight network architectures in the asteroid context.
Our proposed feature extractors and their evaluation leverages both synthetic
images and real-world data from missions such as NEAR Shoemaker, Hayabusa,
Rosetta, and OSIRIS-REx. Our contributions include a trained feature extractor,
incremental improvements over existing methods, and a pipeline for training
domain-specific feature extractors. Experimental results demonstrate the
effectiveness of our approach in achieving accurate navigation and
localization. This work aims to advance the field of asteroid navigation and
provides insights for future research in this domain.

通过轻量级特征提取器，本研究采用视觉为基础的接近导航方法解决小行星探测任务中的挑战，提供了在照明变化和仿射变换下具有稳健性的特征提取方法，同时将合成图像和实际数据结合以验证其有效性。通过该研究，取得了在小行星导航领域准确导航和定位的成果，并为未来的研究提供了有价值的见解。