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.

翻译：三维场景图通过分层方式恰当组织不同环境层级中的各种实体，从而层次化表征环境。我们先前在情境图方面的工作通过将机器人位姿与场景图实体紧密耦合，将三维场景图概念扩展到SLAM领域，取得了最先进的成果。然而，S-Graph的局限性之一在于大规模环境中，随着时间推移图规模增大导致计算复杂度上升。为克服这一局限，本文提出了一项初步研究——通过利用层级结构压缩图规模，通过边缘化冗余机器人位姿及其与同一结构实体观测的连接来优化S-Graph。首先，我们提出构建并优化房间局部图，该图包含类房间结构内的所有图实体。这些房间局部图用于压缩S-Graph，边缘化给定房间内的冗余机器人关键帧。随后，我们按固定时间-距离间隔对压缩图进行滑动窗口局部优化。每当检测到闭环时，对压缩图执行全局优化。实验表明，相较于基线方法，本方法在保持相近精度的同时，计算时间减少了39.81%。