SCORE：适用于封闭退化环境飞行的形状适配区域
SCORE: Shape-Conforming Regions for Flight in Enclosed, Degraded Environments
AI总结：
该研究针对封闭退化环境中自主无人机的避障问题，提出基于符号距离场（SDF）的形状适配非凸禁入区域，结合互补传感器与可见性适配机制，提升了可用空间与飞行安全性。
AI中文摘要：
自主无人机会进入洞穴、坍塌结构等封闭环境，这类环境会限制飞行器并导致感知性能退化。保形预测提供了一种无分布保证，其通过校准障碍物禁入区域需扩展的距离，以在目标覆盖水平下吸收感知误差。然而，现有禁入区域采用凸基元，其凸起会占用狭窄通道，且随感知性能退化而扩大。本文的主要贡献是在符号距离场（SDF）上定义非一致性得分，这会生成非凸禁入区域，该区域紧密贴合障碍物几何形状，避免了等间距凸区域不必要的凸起。两个支撑组件确保该几何结构在感知退化时仍可用：其一，互补传感器观测的体素级并集可验证任一单一传感器遗漏的体素；其二，障碍物周围的间距可适配测得的可见性，无需天气标签或单次飞行无法提供的在线真值反馈。真实地下数据的结果表明，所生成的无分布、形状适配禁入区域在相同认证覆盖水平下，比凸基线保留了更多可用自由空间，且能实现更安全的闭环飞行。
英文摘要：
Autonomous UAVs enter enclosed environments such as caves and collapsed structures that confine the vehicle and degrade perception. Conformal prediction provides a distribution-free guarantee by calibrating how far an obstacle keep-out must expand to absorb perception error at a target coverage level. However, existing keep-out regions use convex primitives whose bulges consume narrow passages and grow as perception degrades. Our main contribution defines the nonconformity score on a signed distance field (SDF). This produces a non-convex keep-out that tightly follows obstacle geometry and avoids the unnecessary bulging of equal-margin convex regions. Two supporting components keep this geometry usable as perception degrades. First, a voxelwise union of complementary sensor observations certifies voxels that any single sensor misses. Second, the margin around the obstacle adapts to measured visibility without weather labels or the online ground-truth feedback that single-pass flight cannot provide. Results on real subterranean data show that the resulting distribution-free, shape-conforming keep-out retains more usable free space than convex baselines at the same certified coverage, and produces safer closed-loop flight.
