A Crater Landmark Spatial Information Matching‐Based Non‐Parametric Approach for Autonomous Absolute Localization of Lunar Rovers

ABSTRACT With the resurgence of lunar exploration, long‐range rover traverses in challenging regions such as the Lunar South Pole demand high‐precision autonomous absolute localization, as rugged terrain and extreme illumination limit current ground‐in‐the‐loop methods. Our study proposed a non‐parametric absolute localization approach that used lunar impact craters as landmarks to support high‐precision rover positioning during extended traverses. We developed two crater detection methods to extract craters' spatial information from rover's stereo imagery, i.e., a 3D point cloud‐based method using stereo matching network, and a deep learning‐based method that combined 2D keypoints from object detection with disparity maps. A novel object‐level dual‐constraint crater similarity metric was developed and integrated into the particle filter localization framework for efficient crater spatial information matching against the orbital reference database, thus enabling absolute rover localization. To validate the approach, we built a high‐fidelity simulation environment in Blender, using real topographic data from the Intuitive Machine 1 (IM‐1) Nova‐C Odysseus landing region near the Lunar South Pole with varying illumination condition settings (including different solar elevation and azimuth angles). Extensive experiments demonstrated that the approach achieved accurate and robust localization, with an average absolute error under 1.5 m across a 1058.65‐m traverse. These results showed the potential of the proposed approach for precise lunar rover remote localization and navigation in challenging environments.