  Situational Graphs (S-Graphs) merge geometric models of the environment
generated by Simultaneous Localization and Mapping (SLAM) approaches with 3D
scene graphs into a multi-layered jointly optimizable factor graph. As an
advantage, S-Graphs not only offer a more comprehensive robotic situational
awareness by combining geometric maps with diverse hierarchically organized
semantic entities and their topological relationships within one graph, but
they also lead to improved performance of localization and mapping on the SLAM
level by exploiting semantic information. In this paper, we introduce a
vision-based version of S-Graphs where a conventional \ac{VSLAM} system is used
for low-level feature tracking and mapping. In addition, the framework exploits
the potential of fiducial markers (both visible as well as our recently
introduced transparent or fully invisible markers) to encode comprehensive
information about environments and the objects within them. The markers aid in
identifying and mapping structural-level semantic entities, including walls and
doors in the environment, with reliable poses in the global reference,
subsequently establishing meaningful associations with higher-level entities,
including corridors and rooms. However, in addition to including semantic
entities, the semantic and geometric constraints imposed by the fiducial
markers are also utilized to improve the reconstructed map's quality and reduce
localization errors. Experimental results on a real-world dataset collected
using legged robots show that our framework excels in crafting a richer,
multi-layered hierarchical map and enhances robot pose accuracy at the same
time.
