UAV navigation in unstructured outdoor environments using passive monocular vision is hindered by the substantial visual domain gap between simulation and reality. While 3D Gaussian Splatting enables photorealistic scene reconstruction from real-world data, existing methods inherently couple static lighting with geometry, severely limiting policy generalization to dynamic real-world illumination. In this paper, we propose a novel end-to-end reinforcement learning framework designed for effective zero-shot transfer to unstructured outdoors. Within a high-fidelity simulation grounded in real-world data, our policy is trained to map raw monocular RGB observations directly to continuous control commands. To overcome photometric limitations, we introduce Relightable 3D Gaussian Splatting, which decomposes scene components to enable explicit, physically grounded editing of environmental lighting within the neural representation. By augmenting training with diverse synthesized lighting conditions ranging from strong directional sunlight to diffuse overcast skies, we compel the policy to learn robust, illumination-invariant visual features. Extensive real-world experiments demonstrate that a lightweight quadrotor achieves robust, collision-free navigation in complex forest environments at speeds up to 10 m/s, exhibiting significant resilience to drastic lighting variations without fine-tuning.

翻译：在非结构化室外环境中使用被动单目视觉进行无人机导航，主要障碍在于仿真与现实之间存在显著的视觉域差异。尽管三维高斯泼溅技术能够从真实世界数据实现照片级逼真的场景重建，但现有方法本质上将静态光照与几何结构耦合在一起，严重限制了策略在动态真实世界光照条件下的泛化能力。本文提出了一种新颖的端到端强化学习框架，旨在实现向非结构化室外环境的有效零样本迁移。在基于真实世界数据构建的高保真仿真环境中，我们的策略被训练为直接将原始单目RGB观测映射到连续控制指令。为克服光度学限制，我们引入了可重光照三维高斯泼溅技术，该技术通过分解场景组件，使得能够在神经表征内部对环境光照进行显式、基于物理的编辑。通过使用从强烈定向阳光到漫射阴天等多种合成光照条件增强训练，我们迫使策略学习鲁棒的、对光照不变的视觉特征。大量真实世界实验表明，轻量级四旋翼无人机能够在复杂森林环境中以高达10米/秒的速度实现鲁棒、无碰撞导航，并在未经微调的情况下对剧烈光照变化展现出显著的适应能力。