月球表面的光照变化对航天器着陆点定位带来了巨大挑战。传统的图像处理技术，如直方图均衡化和基于Retinex的方法，无法适应行星表面多变的光照环境。本文提出了一种融合光照控制算法LomFormer(Light on Moon)的着陆点定位流程，LomFormer算法基于Transformer，旨在将某一光照条件下的行星表面图像控制为另一光照角度，以解决基于视觉的着陆点定位流程中，月面遥感图像和着陆相机图像可能存在的光照不一致而导致无法匹配的问题。为对该模型进行训练，本文基于月表真实高程图数据进行多光照角度渲染，以生成多角度光照月表数据集。通过训练和验证，所提出的方法在真实数据中表现出良好的性能，有效增强了月表匹配的精度，且在不同光照条件下表现出强大的鲁棒性。该研究为解决航天器着陆点定位中的光照挑战提供了新的角度，并为未来的自主行星探测任务中的匹配建图任务提出了新的思路。

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.