A Review of Deep Space Image-Based Navigation Methods

Deep space exploration missions face technical challenges such as long-distance communication delays and high-precision autonomous positioning. Traditional ground-based telemetry and control as well as inertial navigation schemes struggle to meet mission requirements in the complex environment of deep space. As a vision-based autonomous navigation technology, image-based navigation enables spacecraft to obtain real-time images of the target celestial body surface through a variety of onboard remote sensing devices, and it achieves high-precision positioning using stable terrain features, demonstrating good autonomy and adaptability. Craters, due to their stable geometry and wide distribution, serve as one of the most important terrain features in deep space image-based navigation and have been widely adopted in practical missions. This paper systematically reviews the research progress of deep space image-based navigation technology, with a focus on the main sources of remote sensing data and a comprehensive summary of its typical applications in lunar, Martian, and asteroid exploration missions. Focusing on key technologies in image-based navigation, this paper analyzes core methods such as surface feature detection, including the accurate identification and localization of craters as critical terrain features in deep space exploration. On this basis, the paper further discusses possible future directions of image-based navigation technology in response to key challenges such as the scarcity of remote sensing data, limited computing resources, and environmental noise in deep space, including the intelligent evolution of image navigation systems, enhanced perception robustness in complex environments, hardware evolution of autonomous navigation systems, and cross-mission adaptability and multi-body generalization, providing a reference for subsequent research and engineering practice.