Vision-based Situational Graphs Generating Optimizable 3D Scene Representations

3D scene graphs offer a more efficient representation of the environment by
hierarchically organizing diverse semantic entities and the topological
relationships among them. Fiducial markers, on the other hand, offer a valuable
mechanism for encoding comprehensive information pertaining to environments and
the objects within them. In the context of Visual SLAM (VSLAM), especially when
the reconstructed maps are enriched with practical semantic information, these
markers have the potential to enhance the map by augmenting valuable semantic
information and fostering meaningful connections among the semantic objects. In
this regard, this paper exploits the potential of fiducial markers to
incorporate a VSLAM framework with hierarchical representations that generates
optimizable multi-layered vision-based situational graphs. The framework
comprises a conventional VSLAM system with low-level feature tracking and
mapping capabilities bolstered by the incorporation of a fiducial marker map.
The fiducial markers aid in identifying walls and doors in the environment,
subsequently establishing meaningful associations with high-level entities,
including corridors and rooms. Experimental results are conducted on a
real-world dataset collected using various legged robots and benchmarked
against a Light Detection And Ranging (LiDAR)-based framework (S-Graphs) as the
ground truth. Consequently, our framework not only excels in crafting a richer,
multi-layered hierarchical map of the environment but also shows enhancement in
robot pose accuracy when contrasted with state-of-the-art methodologies.