仅用单目摄像头实现复杂环境飞行，靠仿真+域适应突破真实世界应用瓶颈。
Flying in Clutter on Monocular RGB by Learning in 3D Radiance Fields with Domain Adaptation
- 在3D高斯泼溅仿真中训练，用对抗域适应缩小仿真与真实差异
- 零样本迁移至物理世界，在不同光照下实现安全敏捷飞行
- 适合做低成本视觉导航的无人机系统研发者参考
现代自主导航系统主要依赖激光雷达和深度相机。然而一个根本性问题仍未解决：仅使用单目RGB图像的飞行机器人能否在复杂环境中导航？由于真实数据采集成本高昂，通过仿真学习政策是一条有前景的路径。但直接将此类策略部署到物理世界时，会受到显著的仿真到现实感知差距阻碍。因此，我们提出一种框架，结合3D高斯泼溅（3DGS）环境的逼真度与对抗域适应技术。通过在高保真仿真中训练，并显式最小化特征差异，我们的方法确保策略依赖于域不变特征。实验结果表明，该策略实现了对物理世界的鲁棒零样本迁移，使机器人能够在光照变化的非结构化环境中实现安全且敏捷的飞行。
原文摘要 · Abstract (English)
Modern autonomous navigation systems predominantly rely on lidar and depth cameras. However, a fundamental question remains: Can flying robots navigate in clutter using solely monocular RGB images? Given the prohibitive costs of real-world data collection, learning policies in simulation offers a promising path. Yet, deploying such policies directly in the physical world is hindered by the significant sim-to-real perception gap. Thus, we propose a framework that couples the photorealism of 3D Gaussian Splatting (3DGS) environments with Adversarial Domain Adaptation. By training in high-fidelity simulation while explicitly minimizing feature discrepancy, our method ensures the policy relies on domain-invariant cues. Experimental results demonstrate that our policy achieves robust zero-shot transfer to the physical world, enabling safe and agile flight in unstructured environments with varying illumination.
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