用可编辑高斯点云构建仿真环境，让无人机视觉导航更鲁棒
Performance-Guided Refinement for Visual Aerial Navigation using Editable Gaussian Splatting in FalconGym 2.0
- 基于可编程编辑的高斯点云生成多样飞行路径
- 单一策略在3条未见路径上100%成功且抗干扰能力强
- 支持零样本从仿真到真实硬件的直接部署
视觉策略对飞行导航至关重要。现有方法常过度拟合单一路径，路径变化时性能下降。我们构建了FalconGym 2.0——一个基于高斯点云（GSplat）的逼真仿真框架，通过编辑接口可在毫秒内生成多样静态与动态路径。利用其可编辑性，提出性能引导精炼（PGR）算法，聚焦于挑战性路径训练并持续提升性能。在固定翼无人机与四旋翼两类不同动力学和环境的案例中，使用PGR训练的单一视觉策略在泛化性和鲁棒性上优于现有基线：在三条未见过的路径上实现100%成功率，无需每条路径重新训练；在门位姿扰动下仍保持更高成功率。最后，该策略在真实四旋翼上实现零样本模拟到现实迁移，在30次试验（两条三门路径与一条动门路径）中成功通过69/70个门，成功率达98.6%。
原文摘要 · Abstract (English)
Visual policy design is crucial for aerial navigation. However, state-of-the-art visual policies often overfit to a single track and their performance degrades when track geometry changes. We develop FalconGym 2.0, a photorealistic simulation framework built on Gaussian Splatting (GSplat) with an Edit API that programmatically generates diverse static and dynamic tracks in milliseconds. Leveraging FalconGym 2.0's editability, we propose a Performance-Guided Refinement (PGR) algorithm, which concentrates visual policy's training on challenging tracks while iteratively improving its performance. Across two case studies (fixed-wing UAVs and quadrotors) with distinct dynamics and environments, we show that a single visual policy trained with PGR in FalconGym 2.0 outperforms state-of-the-art baselines in generalization and robustness: it generalizes to three unseen tracks with 100% success without per-track retraining and maintains higher success rates under gate-pose perturbations. Finally, we demonstrate that the visual policy trained with PGR in FalconGym 2.0 can be zero-shot sim-to-real transferred to a quadrotor hardware, achieving a 98.6% success rate (69 / 70 gates) over 30 trials spanning two three-gate tracks and a moving-gate track.
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