Neural Network-Aided Optical Navigation for Precise Lunar Descent Operations

Advanced navigation capabilities are essential for precise landing operations, enabling access to critical lunar sites and supporting future lunar infrastructure. To achieve accurate positioning, innovative navigation methods leveraging neural network frameworks are being developed to detect distinctive lunar surface features, such as craters, from imaging data. By matching detected features with known landmarks stored in an onboard reference database, key navigation measurements are retrieved to refine the spacecraft trajectory, enabling real-time planning for hazard avoidance. This work presents a crater-based navigation system for planetary descent operations, which leverages a robust machine learning approach for crater detection in optical images. A thorough analysis of the attainable detection accuracies was performed by evaluating the network performance on diverse sets of synthetic images rendered at different illumination conditions through a custom Blender-based pipeline. Simulation campaigns, based on the JAXA Smart Lander for Investigating Moon mission, were then carried out to demonstrate the system’s performance, achieving final position errors consistent with 3 − σ uncertainties lower than 100 m on the horizontal plane at altitudes as low as 10 km. This level of accuracy is key to achieving enhanced control during the approach and vertical descent phases, thereby ensuring operational safety and facilitating precise landing.