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 leveraging 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 approaches. 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.

翻译：近期关于SLAM的研究通过利用高层语义概念（如房间）及其关系来扩展位姿图，不仅提供了更丰富的环境/场景表征，还提升了估计精度。具体而言，我们先前的研究——情境图（S-Graphs+），作为在因子优化过程中联合利用语义关系的先驱工作，依赖于平面和房间等数学关系已定义的语义实体。然而，目前尚缺乏统一方法来发现低层因子图中对应不同性质高层概念的所有隐藏模式。当前采用的特设算法限制了图的表达能力。为突破这一局限，本文提出一种基于图神经网络的高层语义关系概念学习算法，该算法可从低层因子图中推断高层语义关系。给定一组已建图的平面实体，我们的算法能够推断出与这些平面相关联的房间实体。此外，为展示方法的通用性，该算法还可推断另一语义关系概念（即墙面）及其与平面的关联。我们在模拟和真实数据集上验证了该方法，表明其性能优于两种基线方法。进一步地，将该方法集成至S-Graphs+算法中，相比基线方法显著提升了位姿与地图精度，同时增强了场景表征能力。