提出形状贴合的飞行安全区，提升洞穴等封闭环境下的无人机避障能力。
SCORE: Shape-Conforming Regions for Flight in Enclosed, Degraded Environments
- 基于符号距离场定义非凸安全区，贴合障碍物真实形状。
- 在相同覆盖率下，比传统凸形安全区多保留23%可用空域。
- 无需地面真值反馈，适应感知退化，适合真实地下场景飞行。
自主无人机进入洞穴和坍塌结构等封闭环境时，受限空间与感知退化带来挑战。传统方法使用凸形保持区域，其过度膨胀会占用狭窄通道，且随感知质量下降而扩大。本文提出在符号距离场（SDF）上定义非一致性得分，生成紧贴障碍物几何形状的非凸安全区，避免了凸形区域的无谓膨胀。两个关键组件保障该几何结构在感知退化时仍可用：首先，通过体素级互补传感器观测融合，确认单个传感器遗漏的区域；其次，安全边距自适应当前可见性，无需天气标签或在线真值反馈。真实地下数据实验表明，所提分布无关、形状贴合的安全区在相同认证覆盖率下，比凸基线保留更多可用自由空间，实现更安全的闭环飞行。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。