Better Situational Graphs by Inferring High-level Semantic-Relational Concepts

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.