通过语义关系增强图匹配，提升复杂场景下的定位精度与效率。
Robust Graph Matching through Semantic Relationship Generation for SLAM
- 利用物体与结构元素的语义关系过滤候选匹配
- 在对称场景中将候选匹配数大幅减少，加速收敛
- 适合需要高鲁棒性定位的室内机器人应用
基于图的表示（如场景图）可通过将传感器数据构建的局部图与先验地图匹配，实现结构化室内环境中的定位。但在重复或对称布局环境中，仅靠结构线索常无法消除歧义。本文提出一种语义增强的图匹配方法，显式建模检测到的物体与结构元素（如房间、墙面平面）之间的关系。物体从RGB-D数据中检测并融入图中，其与结构元素的关系被用于几何验证前筛选候选对应关系，显著降低歧义性和搜索复杂度。该方法集成于iS-Graphs框架，在合成与仿真环境中评估。结果表明，语义关系能显著减少候选匹配数量，提升计算效率，并在纯几何方法失效的对称场景中实现更快收敛。
原文摘要 · Abstract (English)
Graph-based representations such as Scene Graphs enable localization in structured indoor environments by matching a locally observed graph, constructed from sensor data, to a prior map. This process is particularly challenging in environments with repetitive or symmetric layouts, where structural cues alone are often insufficient to resolve ambiguities. We propose a semantic-enhanced graph matching approach that explicitly models relations between detected objects and structural elements, such as rooms and wall planes. Objects are detected from RGB-D data and integrated into the graph, and their relations to structural elements are exploited to filter candidate correspondences prior to geometric verification, significantly reducing ambiguity and search complexity. The proposed method is integrated within the iS-Graphs framework and evaluated in synthetic and simulated environments. Results show that semantic relations significantly reduce the number of candidate matches, improve computational efficiency, and enable faster convergence, particularly in symmetric scenarios where purely geometric approaches fail.
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