SemSafe-3DGS: Semantic Risk-Aware Active Navigation in Uncertain 3D Gaussian Splatting Maps

2026-09-16

AI Summary

研究提出SemSafe-3DGS框架，通过语义风险权重调整避障模型，结合主动感知障碍函数，在不确定的3D高斯地图中实现语义风险感知的安全导航。

Abstract

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.

Problem

• autonomous robots
• partially observed environments
• semantic risk
• active perception
• Gaussian splatting maps

Innovation

• Semantic Risk-Aware
• Active Perception
• Gaussian Splatting Maps
• Control Barrier Function
• Average Value-at-Risk