Lunar Rover Cross-View Localization Through Integration of Rover and Orbital Images

Xinyu Zhao, Linyan Cui, Xiaodong Wei, Chuankai Liu, Jihao Yin

Abstract: Efficient visual localization of lunar rovers is essential for long-range autonomous exploration missions and the construction of the International Lunar Research Station. Given the communication delay and bandwidth limitations between the Earth and the Moon, we propose a novel cross-view localization framework that autonomously registers a single rover image to an orbital image. We developed a bird’s-eye-view (BEV) feature synthesis method that integrates geometric projection, cross-scale feature transfer (CSFT), and contour guidance mechanism (CGM). The basic principle involves first projecting rover features into the BEV perspective through geometric projection, then reconstructing BEV features in reference to orbital features using CSFT. This process is guided by CGM, enhancing the expression of rich terrain contours in BEV and orbital features, thereby improving the viewpoint and scale consistency of cross-view features. By conducting dense spatial correlation searches between BEV and orbital features, we can accurately estimate the position of the lunar rover. Additionally, we introduce a lunar surface simulation environment and construct the lunar cross-view localization (LCVL) simulation dataset based on this environment to demonstrate the framework’s effectiveness. Our research offers a new solution for rover localization, potentially improving the efficiency of future exploration missions.