  Mobile robots extract information from its environment to understand their
current situation to enable intelligent decision making and autonomous task
execution. In our previous work, we introduced the concept of Situation Graphs
(S-Graphs) which combines in a single optimizable graph, the robot keyframes
and the representation of the environment with geometric, semantic and
topological abstractions. Although S-Graphs were built and optimized in
real-time and demonstrated state-of-the-art results, they are limited to
specific structured environments with specific hand-tuned dimensions of rooms
and corridors.
  In this work, we present an advanced version of the Situational Graphs
(S-Graphs+), consisting of the five layered optimizable graph that includes (1)
metric layer along with the graph of free-space clusters (2) keyframe layer
where the robot poses are registered (3) metric-semantic layer consisting of
the extracted planar walls (4) novel rooms layer constraining the extracted
planar walls (5) novel floors layer encompassing the rooms within a given floor
level. S-Graphs+ demonstrates improved performance over S-Graphs efficiently
extracting the room information while simultaneously improving the pose
estimate of the robot, thus extending the robots situational awareness in the
form of a five layered environmental model.
