SemSafe-3DGS：不确定三维高斯溅射地图中的语义风险感知主动导航
SemSafe-3DGS: Semantic Risk-Aware Active Navigation in Uncertain 3D Gaussian Splatting Maps
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中文总结 AI 辅助
提出SemSafe-3DGS框架，在三维高斯地图中通过语义风险权重调节碰撞间隙，结合主动感知屏障，实现安全高效的自主导航，并验证于真实机器人。
中文摘要 AI 辅助
在部分可观测环境中运行的自主机器人必须在安全导航的同时获取观测数据，以改进未来规划。现有的安全公式通常主要考虑几何因素。因此，几何上相似的场景元素可能引发相似的控制响应，尽管它们具有不同的语义后果。我们提出了一种用于带属性三维高斯地图导航的语义风险感知安全主动感知框架。语义属性通过类别相关风险权重调节平均风险价值碰撞间隙模型，使得安全关键的高斯原语在复合屏障中获得更大影响。所得的加权间隙被聚合为控制屏障函数，而轨迹相关的主动感知屏障促进观测，以减少机器人预期运动路径上的几何地图不确定性。这两个目标被集成在一个统一的CBF-QP中，该优化将语义风险感知碰撞避免作为硬约束强制执行，并在信息获取与安全或任务进展冲突时放松信息获取。实验证明了高效的安全约束、通过主动感知改进的导航、语义相关的轨迹适应，以及在阿克曼动力学下的真实机器人执行。
英文摘要
Autonomous robots operating in partially observed environments must navigate safely while acquiring observations that improve future planning. Existing safety formulations generally reason primarily about geometry. Consequently, geometrically similar scene elements may induce comparable control responses despite having different semantic consequences. We present a semantic risk aware safe-active perception framework for navigation in attributed 3D Gaussian maps. Semantic attributes modulate an Average Value-at-Risk collision clearance model through class dependent risk weights, allowing safety-critical Gaussian primitives to receive greater influence in the composite barrier. The resulting weighted clearances are aggregated into a control barrier function, while a trajectory-relevant active perception barrier promotes observations that reduce geometric map uncertainty along the robot's anticipated motion. Both objectives are integrated in a unified CBF-QP that enforces semantic risk-aware collision avoidance as a hard constraint while relaxing information acquisition when it conflicts with safety or task progress. Experiments demonstrate efficient safety constraint, improved navigation through active perception, semantic dependent trajectory adaptation, and real-robot execution under Ackermann dynamics.
发表机构
- Lehigh University（理海大学）
机构由 AI 辅助整理，请以论文原文为准。
