Passage-Aware Structural Mapping for RGB-D Visual SLAM
2026-04-27 • Robotics
Robotics
AI summaryⓘ
The authors developed a method to help indoor robots better understand doorways and passages using RGB-D cameras. Their approach combines shape, meaning, and layout info to find doors and tell if they can be walked through. They test their method by adding it to an existing system, which helps show how rooms connect. Their tests in office environments show the method finds doorways reliably, helping future robot navigation and mapping work.
Visual SLAMRGB-D cameradoorway detectionsemantic cuesgeometric cuestopological cuesscene graphtraversable passagesvS-GraphsBIM
Authors
Ali Tourani, Miguel Fernandez-Cortizas, Saad Ejaz, David Pérez Saura, Asier Bikandi-Noya, Jose Luis Sanchez-Lopez, Holger Voos
Abstract
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.