将3D高斯点云转为安全的凸多面体，实现实时避障导航
PolyMerge: Compressing 3D Gaussian Splats with Polytope Coverings for Provably Safe Resource-Constrained Navigation
- 用凸多面体覆盖原3D高斯模型，保证障碍物被完全包含
- 在无人机上实时计算安全路径，比基线快且确保不碰撞
- 适合资源受限的机器人导航场景，兼具安全性与效率
障碍物避让对安全导航至关重要。近年来的辐射场重建方法虽能实现高保真物体建模，但内存和计算开销过大，难以用于机载感知路径规划。为此，我们提出PolyMerge，将大型、逼真的3D高斯点云（3DGS）模型转换为一组凸多面体的轻量级表示，其并集可严格包络原3DGS模型中的所有障碍物。PolyMerge通过调节多面体数量，在保守性与计算成本间实现权衡，并与控制屏障函数（CBFs）结合，用于生成无碰撞路径。我们在模拟及硬件实验中验证了该方法，使用Crazyflie无人机在严重算力限制下实时计算并执行安全轨迹，性能优于基线，同时保障安全性。
原文摘要 · Abstract (English)
Obstacle avoidance is essential for safe navigation and motion planning. Recent radiance field reconstruction methods enable object detection and modeling with high fidelity, but remain too memory- and compute-intensive for on-board perception-based path planning. To address these limitations, we propose PolyMerge to convert a large, photorealistic 3D Gaussian Splatting (3DGS) model of a scene into a lightweight representation of convex polytopes whose union provably over-approximates all obstacles in the original 3DGS model. PolyMerge tunes the polytope count to trade off conservativeness and compute cost, and integrates with control barrier functions (CBFs) to plan collision-free paths. We showcase PolyMerge in simulation and hardware experiments on a Crazyflie drone, which uses PolyMerge to compute and follow safe trajectories in real time under severe onboard compute constraints, outperforming baselines in speed while guaranteeing safety. For our code and videos, visit https://athlon76.github.io/PolyMerge-website/.
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