对比开源图像模拟方案，提升月球环境合成图像质量
Evaluation of image simulation open source solutions for simulation of synthetic images in lunar environment
- 基于真实月面地形数据与相机模型生成合成图像
- 验证不同光照和相机参数对图像质量的影响
- 助力月球自主导航与任务规划系统可靠性提升
合成图像生成是行星任务的关键输入，可帮助研究人员和工程师在实际部署前虚拟可视化任务、测试成像系统并规划探索活动。图像模拟对于评估着陆点、探测潜在风险及验证导航系统至关重要。本研究详细评估了多种面向月球环境的图像模拟方法，重点关注不同相机模型和光照条件对合成月面图像质量的影响。图像基于真实数字高程模型（DEM）及来自月船2号轨道器高分辨率相机（OHRC）、NASA宽角相机（WAC）和窄角相机（NAC）的地形数据生成。研究旨在提升合成影像在支持月球探索中自主导航与决策系统的可靠性，为未来月球任务提供更有效的信息生成工具，并深化对月面环境的理解。
原文摘要 · Abstract (English)
Synthetic image generation is one of the crucial input for planetary missions. It enables researchers and engineers to visualize planned planetary missions, test imaging systems and plan exploration activities in a virtual environment before actual deployment. Image simulation is essential for assessing landing sites, detecting hazards, and validating navigation systems in a missions. This study offers a detailed evaluation of various image simulation approaches for the lunar environment, with particular emphasis on the effects of different camera models and light illumination conditions on the quality of synthetic lunar images. These images are produced using real Digital Elevation Models (DEM) and terrain data derived from instruments such as Chandrayaan-2 Orbiter High Resolution Camera (OHRC) and NASA's Wide Angle Camera (WAC), and Narrow Angle Camera (NAC) instruments. This research aims to improve the reliability of synthetic imagery in supporting autonomous navigation and decision-making systems in lunar exploration. This work contributes to the development of more effective tools for generating important information for future lunar missions and enhances the understanding of the moon's surface environment.
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