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

TL;DR

This paper introduces TACS-Graphs, a novel framework that improves indoor scene graph segmentation for ground robots by incorporating traversability, leading to more accurate and consistent environment representations and enhanced localization.

Contribution

The work is the first to address segmentation inconsistency in scene graphs by integrating traversability, resulting in more coherent room segmentation and improved loop closure detection.

Findings

Outperforms state-of-the-art in scene graph consistency

Enhances pose estimation accuracy

Improves loop closure detection efficiency

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. 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.