Abstract: The significant illumination variations on the lunar surface pose a substantial challenge to spacecraft landing site localization. Traditional image processing techniques, such as histogram equalization and Retinex-based methods, struggle to adapt to the highly variable lighting conditions prevalent on planetary surfaces. To address the issue of illumination inconsistency between orbital remote sensing images and descent camera images—which often leads to matching failures in vision-based landing site localization pipelines—this paper proposes a novel localization framework that integrates a dedicated illumination control algorithm named LomFormer (Light on Moon). Based on a Transformer architecture, LomFormer is designed to actively control the illumination angle in planetary surface images, effectively transforming an image captured under one lighting condition to appear as if it were taken under another. For model training, a multi-angle illumination dataset of the lunar surface was generated by rendering images based on real lunar Digital Elevation Model (DEM) data under various lighting azimuths and elevations. Training and validation results demonstrate that the proposed method achieves promising performance on real data, significantly enhancing the accuracy of lunar image matching and exhibiting strong robustness across diverse illumination scenarios. This research provides a novel perspective on tackling illumination-related challenges in spacecraft landing site localization and introduces a new paradigm for mapping and localization tasks in future autonomous planetary exploration missions.

Key words: planetary exploration, illumination control, image matching, landing-site localization, dataset, deep learning