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.

翻译：门廊和通道是室内机器人导航的关键结构元素，但在现代视觉SLAM（VSLAM）框架中仍未得到充分研究。本文提出一种面向RGB-D VSLAM的通道感知结构映射方法，通过融合几何、语义和拓扑线索联合检测门与可通行开口。门被建模为嵌入墙体中的平面实体，并根据其与支撑墙体的共面性分类为可通行或不可通行。通道通过两种互补策略推断：连续关键帧之间相机-墙体相互作用积累的通行证据，以及基于映射墙几何不连续性的几何开口验证。作为概念验证，所提方法被集成至vS-Graphs，通过通道级抽象丰富其场景图并改进房间连通性建模。在室内办公序列上的定性评估展示了可靠的门洞检测能力，该框架为在建筑信息模型（BIM）辅助的VSLAM中利用这些元素奠定了基础。源代码已开源并发布于 https://github.com/snt-arg/visual_sgraphs/tree/doorway_integration。