TACS-Graphs: Traversability-Aware Consistent Scene Graphs for Ground Robot Localization and Mapping

Abstract-- 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. T o the best of our knowledge, this work is the first to tackle segmentation inconsistency as a challenge and address it with Traversability-A ware Consistent Scene Graphs (T ACS-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.