让机器人识别门洞，提升室内导航的结构理解能力
Passage-Aware Structural Mapping for RGB-D Visual SLAM
- 融合几何、语义与拓扑信息检测门和可通行通道
- 通过连续关键帧交互与墙面几何断裂验证推断通行路径
- 适合需要精准房间连通性建模的机器人导航场景
门洞是室内机器人导航的关键结构性元素，但在现代视觉里程计（VSLAM）框架中仍被忽视。本文提出一种面向RGB-D VSLAM的门洞感知结构建图方法，通过联合融合几何、语义与拓扑线索，检测门和可通行开口。门被建模为嵌入墙中的平面实体，并根据其与墙体的共面性判断是否可通行。通道通过两种互补策略推断：基于连续关键帧间相机-墙交互累积的通行证据，以及基于映射墙面几何不连续性的几何开口验证。该方法集成至vS-Graphs作为概念验证，丰富了场景图中的门洞级抽象，提升了房间连通性建模能力。在办公环境序列上的定性评估表明，门洞检测可靠，该框架为在建筑信息模型（BIM）引导的VSLAM中利用这些元素奠定了基础。源码已公开于https://github.com/snt-arg/visual_sgraphs/tree/doorway_integration。
原文摘要 · Abstract (English)
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.
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