面向RGB-D视觉SLAM的通道感知结构映射
Passage-Aware Structural Mapping for RGB-D Visual SLAM
AI总结：
本文提出一种通道感知的RGB-D视觉SLAM结构映射方法，通过融合几何、语义和拓扑信息检测门和可通行开口，提升场景图中房间连通性建模。
摘要：
门和通道是室内机器人导航的关键结构元素，但现代视觉SLAM（VSLAM）框架中仍缺乏深入研究。本文提出了一种面向RGB-D视觉SLAM的通道感知结构映射方法，通过联合融合几何、语义和拓扑线索来检测门和可通行开口。门被建模为嵌入在墙内的平面实体，并根据其与支撑墙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：从连续关键帧中累积的相机-墙交互证据，以及基于映射墙几何中不连续性的几何开口验证。所提方法被集成到vS-Graphs中作为概念验证，丰富其场景图并提升房间连通性建模。在室内办公室序列上的定性评估显示了可靠的门检测，该框架为在BIM指导的VSLAM中利用这些元素奠定了基础。源代码可在https://github.com/snt-arg/visual_sgraphs/tree/doorway_integration公开获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
