vS-Graphs: Integrating Visual SLAM and Situational Graphs through Multi-level Scene Understanding
Journal:
arXiv
Published Date:
Mar 3, 2025
Abstract
Current Visual Simultaneous Localization and Mapping (VSLAM) systems often
struggle to create maps that are both semantically rich and easily
interpretable. While incorporating semantic scene knowledge aids in building
richer maps with contextual associations among mapped objects, representing
them in structured formats like scene graphs has not been widely addressed,
encountering complex map comprehension and limited scalability. This paper
introduces visual S-Graphs (vS-Graphs), a novel real-time VSLAM framework that
integrates vision-based scene understanding with map reconstruction and
comprehensible graph-based representation. The framework infers structural
elements (i.e., rooms and corridors) from detected building components (i.e.,
walls and ground surfaces) and incorporates them into optimizable 3D scene
graphs. This solution enhances the reconstructed map's semantic richness,
comprehensibility, and localization accuracy. Extensive experiments on standard
benchmarks and real-world datasets demonstrate that vS-Graphs outperforms
state-of-the-art VSLAM methods, reducing trajectory error by an average of
3.38% and up to 9.58% on real-world data. Furthermore, the proposed framework
achieves environment-driven semantic entity detection accuracy comparable to
precise LiDAR-based frameworks using only visual features. A web page
containing more media and evaluation outcomes is available on
https://snt-arg.github.io/vsgraphs-results/.
