Scene graphs have emerged as a powerful tool for robots, providing a structured representation of spatial and semantic relationships for advanced task planning. Despite their potential, conventional 3D indoor scene graphs face critical limitations, particularly under- and over-segmentation of room layers in structurally complex environments. Under-segmentation misclassifies non-traversable areas as part of a room, often in open spaces, while over-segmentation fragments a single room into overlapping segments in complex environments. These issues stem from naive voxel-based map representations that rely solely on geometric proximity, disregarding the structural constraints of traversable spaces and resulting in inconsistent room layers within scene graphs. To the best of our knowledge, this work is the first to tackle segmentation inconsistency as a challenge and address it with Traversability-Aware Consistent Scene Graphs (TACS-Graphs), a novel framework that integrates ground robot traversability with room segmentation. By leveraging traversability as a key factor in defining room boundaries, the proposed method achieves a more semantically meaningful and topologically coherent segmentation, effectively mitigating the inaccuracies of voxel-based scene graph approaches in complex environments. Furthermore, the enhanced segmentation consistency improves loop closure detection efficiency in the proposed Consistent Scene Graph-leveraging Loop Closure Detection (CoSG-LCD) leading to higher pose estimation accuracy. Experimental results confirm that the proposed approach outperforms state-of-the-art methods in terms of scene graph consistency and pose graph optimization performance.

翻译：场景图已成为机器人的强大工具，为高级任务规划提供空间与语义关系的结构化表示。尽管潜力巨大，传统三维室内场景图仍面临关键局限，尤其是在结构复杂环境中房间层级的欠分割与过分割问题。欠分割将不可通行区域（常见于开放空间）误分类为房间的一部分，而过分割则在复杂环境中将单个房间分割为重叠片段。这些问题源于仅依赖几何邻近性的朴素体素地图表示方法，忽略了可通行空间的结构约束，导致场景图中房间层级的不一致。据我们所知，本研究首次将分割不一致性作为挑战性问题，并通过可通行性感知一致性场景图（TACS-Graphs）予以解决——这是一个将地面机器人可通行性与房间分割相融合的新型框架。通过将可通行性作为定义房间边界的关键因素，所提方法实现了更具语义意义和拓扑一致性的分割，有效缓解了体素基场景图方法在复杂环境中的不准确性。此外，增强的分割一致性提升了所提一致性场景图增强闭环检测（CoSG-LCD）的闭环检测效率，从而获得更高的位姿估计精度。实验结果证实，所提方法在场景图一致性与位姿图优化性能方面均优于现有最先进方法。