Generation of Uncertainty-Aware Emergent Concepts in Factorized 3D Scene Graphs via Graph Neural Networks

Abstract

Enabling robots to autonomously discover emergent spatial concepts (e.g., rooms) from primitive geometric observations (e.g., planar surfaces) within 3D Scene Graphs is essential for robust indoor navigation and mapping. These graphs provide a hierarchical metric-semantic representation in which such concepts are organized. To further enhance graph-SLAM performance, Factorized 3D Scene Graphs incorporate these concepts as optimization factors that constrain relative geometry and enforce global consistency. However, both stages of this process remain largely manual: concepts are typically derived using hand-crafted, concept-specific heuristics, while factors and their covariances are likewise manually designed. This reliance on manual specification limits generalization across diverse environments and scalability to new concept classes. This paper presents, for the first time, a learning-based method to generate online spatial emergent concepts as optimizable factors within a SLAM backend, reducing the need to handcraft both concept generation and the definition of their corresponding factors and covariances. In both simulated and real indoor scenarios, our approach improves complex concept detection by 20.7% and 5.3%, trajectory estimation by 19.2%, and map reconstruction by 12.3% and 3.8%, respectively, highlighting the benefits of this integration for robust and adaptive spatial understanding.