This survey provides a comprehensive overview of recent advancements and challenges in Artificial Intelligence (AI)-enabled computer vision (CV) techniques for space robotic missions, spanning critical phases such as Entry, Descent, and Landing (EDL), orbital operations, and planetary surface exploration. Emphasis is placed on deep-learning–based approaches for image classification, object detection, semantic segmentation, relative pose estimation, and feature matching. State-of-the-art methods in terrain-relative navigation, crater-based or rock-feature matching, and pose estimation for uncooperative targets are highlighted, illustrating the progress achieved through hybrid pipelines combining deep neural networks with classical geometry. The paper also critically evaluates publicly available orbital and planetary data sets—along with the increasing role of synthetic data—for developing and benchmarking CV algorithms under strict resource limitations and harsh environmental conditions.
