Recent works on SLAM extend their pose graphs with higher-level semantic
concepts 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 leveraging semantic relationships in the factor optimization
process, relies on semantic entities such as wall surfaces and rooms, whose
relationship is mathematically defined. Nevertheless, excerpting these
high-level concepts relying exclusively on the lower-level factor-graph remains
a challenge and it is currently done with ad-hoc algorithms, which limits its
capability to include new semantic-relational concepts. To overcome this
limitation, in this work, we propose a Graph Neural Network (GNN) for learning
high-level semantic-relational concepts that can be inferred from the low-level
factor graph. We have demonstrated that we can infer room entities and their
relationship to the mapped wall surfaces, more accurately and more
computationally efficient than the baseline algorithm. Additionally, to
demonstrate the versatility of our method, we provide a new semantic concept,
i.e. wall, and its relationship with its wall surfaces. Our proposed method has
been integrated into S-Graphs+, and it has been validated in both simulated and
real datasets. A docker container with our software will be made available to
the scientific community
