SG-Reg

In this work, we learn to register two semantic scene graphs, an essential capability when an autonomous agent needs to register its map against a remote agent, or against a prior map. To acehive a generalizable registration in the real-world, we design a scene graph network to encode multiple modalities of semantic nodes: open-set semantic feature, local topology with spatial awareness, and shape feature. SG-Reg represents a dense indoor scene in coarse node features and dense point features. In multi-agent SLAM systems, this representation supports both coarse-to-fine localization and bandwidth-efficient communication. We generate semantic scene graph using vision foundation models and semantic mapping module FM-Fusion. It eliminates the need for ground-truth semantic annotations, enabling fully self-supervised network training. We evaluate our method using real-world RGB-D sequences: ScanNet, 3RScan and self-collected data using Realsense i-435.