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.

翻译：基于图的表示（如场景图）通过将从传感器数据构建的局部观测图与先验地图进行匹配，能够在结构化室内环境中实现定位。在重复或对称布局的环境中，仅依赖结构线索往往不足以消除歧义，这使得该过程尤为具有挑战性。我们提出了一种语义增强的图匹配方法，显式建模检测对象与结构元素（如房间和墙面平面）之间的关系。该方法从RGB-D数据中检测对象并将其集成到图中，利用对象与结构元素之间的关系在几何验证前过滤候选匹配，从而显著降低歧义性和搜索复杂度。所提方法集成在iS-Graphs框架内，并在合成与仿真环境中进行验证。结果表明，语义关系显著减少了候选匹配数量，提升了计算效率并加速了收敛，尤其在纯几何方法失效的对称场景中表现突出。