Faster Optimization in S-Graphs Exploiting Hierarchy

3D scene graphs hierarchically represent the environment appropriately
organizing different environmental entities in various layers. Our previous
work on situational graphs extends the concept of 3D scene graph to SLAM by
tightly coupling the robot poses with the scene graph entities, achieving
state-of-the-art results. Though, one of the limitations of S-Graphs is
scalability in really large environments due to the increased graph size over
time, increasing the computational complexity.
To overcome this limitation in this work we present an initial research of an
improved version of S-Graphs exploiting the hierarchy to reduce the graph size
by marginalizing redundant robot poses and their connections to the
observations of the same structural entities. Firstly, we propose the
generation and optimization of room-local graphs encompassing all graph
entities within a room-like structure. These room-local graphs are used to
compress the S-Graphs marginalizing the redundant robot keyframes within the
given room. We then perform windowed local optimization of the compressed graph
at regular time-distance intervals. A global optimization of the compressed
graph is performed every time a loop closure is detected. We show similar
accuracy compared to the baseline while showing a 39.81% reduction in the
computation time with respect to the baseline.