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This paper introduces a passage-aware RGB-D Visual SLAM approach that enhances scene understanding by explicitly detecting doorways and traversable openings. It fuses geometric, semantic, and topological cues to model doors as planar entities and infers passages through camera-wall interaction analysis and geometric opening validation. Integrated into vS-Graphs, the method improves room connectivity modeling and demonstrates reliable doorway detection on indoor office sequences.
Robots can now "see" and understand doorways, enabling more robust navigation in complex indoor environments.
Doorways and passages are critical structural elements for indoor robot navigation, yet they remain underexplored in modern Visual SLAM (VSLAM) frameworks. This paper presents a passage-aware structural mapping approach for RGB-D VSLAM that detects doors and traversable openings by jointly fusing geometric, semantic, and topological cues. Doors are modeled as planar entities embedded within walls and classified as traversable or non-traversable based on their coplanarity with the supporting wall. Passages are inferred through two complementary strategies: traversal evidence accumulated from camera-wall interactions across consecutive keyframes, and geometric opening validation based on discontinuities in the mapped wall geometry. The proposed method is integrated into vS-Graphs as a proof of concept, enriching its scene graph with passage-level abstractions and improving room connectivity modeling. Qualitative evaluations on indoor office sequences demonstrate reliable doorway detection, and the framework lays the foundation for exploiting these elements in BIM-informed VSLAM. The source code is publicly available at https://github.com/snt-arg/visual_sgraphs/tree/doorway_integration.
