Passage-Aware Structural Mapping for RGB-D Visual SLAM
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866918470220775424 |
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| author | Tourani, Ali Fernandez-Cortizas, Miguel Ejaz, Saad Saura, David Pérez Bikandi-Noya, Asier Sanchez-Lopez, Jose Luis Voos, Holger |
| author_facet | Tourani, Ali Fernandez-Cortizas, Miguel Ejaz, Saad Saura, David Pérez Bikandi-Noya, Asier Sanchez-Lopez, Jose Luis Voos, Holger |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_24707 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Passage-Aware Structural Mapping for RGB-D Visual SLAM Tourani, Ali Fernandez-Cortizas, Miguel Ejaz, Saad Saura, David Pérez Bikandi-Noya, Asier Sanchez-Lopez, Jose Luis Voos, Holger Robotics 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. |
| title | Passage-Aware Structural Mapping for RGB-D Visual SLAM |
| topic | Robotics |
| url | https://arxiv.org/abs/2604.24707 |