Structural-Semantic Topological Graph Matching and Map Alignment in Challenging Indoor Scenes

Capturing semantic and structural attributes of objects, together with their spatial relationships, is essential for achieving human-like scene understanding, thereby enhancing robotic tasks such as object association, loop closure detection, and map alignment in complex indoor environments. However, most existing object-based methods oversimplify geometric structures and spatial topology, resulting in coarse scene descriptions and inaccurate or even failed results in the above tasks. To address these limitations, this article presents a novel structural-semantic topological graph matching and map alignment method that fully utilizes scene structural, semantic, and topological cues to improve the performance of the above tasks. Specifically, we introduce a structural–semantic topological graph that jointly models object, plane, and line primitives to obtain a richer scene representation than object-only graphs. Then, we propose a novel instance association method that evaluates comprehensive matching similarity by jointly considering semantic, structural, and topological cues, while adopting a coarse-to-fine strategy to ensure accurate instance correspondences. Finally, we develop a robust pose estimation module that leverages a matches sampling strategy to effectively handle heterogeneous and potential noisy matches. Extensive experiments on the HM3D dataset and our real-world Hallway and Dyn-Room datasets demonstrate that the proposed method significantly outperforms the baselines in object association, loop closure detection, and map alignment.