Robots navigate safer using semantics in 3D uncertainty maps
SemSafe-3DGS: Semantic Risk-Aware Active Navigation in Uncertain 3D Gaussian Splatting Maps
Robotics
Summary
Robots need to move around safely even when they don’t see their whole environment clearly. This work helps robots understand what different parts of a scene mean, not just how they look or where they are. By giving more importance to objects that might cause more serious problems if hit, the robot can plan safer paths and also decide where to look next to improve its map. The authors tested their system on real robots that drive like cars and showed it helps avoid risky situations better while exploring.
What this means in practice
- •For autonomous vehicle engineers: Create safer navigation systems that prioritize avoiding semantically risky obstacles using uncertainty-aware 3D maps.
- •For mobile robot developers: Improve robots’ ability to decide where to look next for better environment understanding while ensuring safe paths in uncertain spaces.
Authors
Amirhossein Mollaei Khass, Athanasios Cosse, Nader Motee
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.