Abstract: A lightweight autonomous lunar orbiter positioning method based on crater visual matching was studied. The goal was to reduce dependence on groundbased tracking systems and improve positioning autonomy and timeliness. Traditional methods relied on ground stations to process radio tracking data, which led to high costs and limited autonomy. To address this, a visual matching algorithm based on neighboring crater clusters was proposed. After dynamically adjusting the onboard camera focal length, geometric information extracted from images was matched with known crater data. The matching results were then used to estimate the orbiter's pose. A filtering process refined the positioning results. Instead of using full crater images, the method built a database using geometric matrices of crater centroids. It matched geometric data from captured images to reduce data load. A lunar-orbiter system model was built. The simulation results show that the proposed crater visual matching algorithm achieves an overall average matching success rate of over 93.24% at different altitudes. The localization error increases with the variance of the added noise. However, after dynamically adjusting the focal length and applying filtering, the localization performance remains robust under various noise conditions. Overall, 92.59% of the localization errors are under one kilometer, and the average positioning error is 0.2727km. These results show that the method enables autonomous positioning using onboard images. It reduces reliance on ground systems and performs well in both matching accuracy and positioning error. The approach shows promise for future autonomous lunar orbiter navigation.

Key words: lightweight, lunar spacecraft, autonomous positioning, feature matching, satellite positioning, pose estimation