Learning High-level Semantic-Relational Concepts for SLAM

Recent works on SLAM extend their pose graphs
with higher-level semantic concepts like Rooms exploiting
relationships between them, to provide, not only a richer
representation of the situation/environment but also to improve
the accuracy of its estimation. Concretely, our previous work,
Situational Graphs (S-Graphs+), a pioneer in jointly leverag-
ing semantic relationships in the factor optimization process,
relies on semantic entities such as Planes and Rooms, whose
relationship is mathematically defined. Nevertheless, there is no
unique approach to finding all the hidden patterns in lower-level
factor-graphs that correspond to high-level concepts of different
natures. It is currently tackled with ad-hoc algorithms, which
limits its graph expressiveness.
To overcome this limitation, in this work, we propose an
algorithm based on Graph Neural Networks for learning high-
level semantic-relational concepts that can be inferred from
the low-level factor graph. Given a set of mapped Planes
our algorithm is capable of inferring Room entities relating
to the Planes. Additionally, to demonstrate the versatility of
our method, our algorithm can infer an additional semantic-
relational concept, i.e. Wall, and its relationship with its Planes.
We validate our method in both simulated and real datasets
demonstrating improved performance over two baseline ap-
proaches. Furthermore, we integrate our method into the S-
Graphs+ algorithm providing improved pose and map accuracy
compared to the baseline while further enhancing the scene
representation.