In this paper, we present an evolved version of Situational Graphs, which jointly models in a single optimizable factor graph (1) a pose graph, as a set of robot keyframes comprising associated measurements and robot poses, and (2) a 3D scene graph, as a high-level representation of the environment that encodes its different geometric elements with semantic attributes and the relational information between them. Specifically, our S-Graphs+ is a novel four-layered factor graph that includes: (1) a keyframes layer with robot pose estimates, (2) a walls layer representing wall surfaces, (3) a rooms layer encompassing sets of wall planes, and (4) a floors layer gathering the rooms within a given floor level. The above graph is optimized in real-time to obtain a robust and accurate estimate of the robots pose and its map, simultaneously constructing and leveraging high-level information of the environment. To extract this high-level information, we present novel room and floor segmentation algorithms utilizing the mapped wall planes and free-space clusters. We tested S-Graphs+ on multiple datasets, including simulated and real data of indoor environments from varying construction sites, and on a real public dataset of several indoor office areas. On average over our datasets, S-Graphs+ outperforms the accuracy of the second-best method by a margin of 10.67%, while extending the robot situational awareness by a richer scene model. Moreover, we make the software available as a docker file.

翻译：本文提出情境图（Situational Graphs）的进化版本，该模型在单个可优化因子图中联合建模：（1）位姿图，即一组包含关联观测和机器人位姿的关键帧集合；（2）三维场景图，即环境的高级表示，其编码具有语义属性的不同几何元素及它们之间的关联信息。具体而言，我们的S-Graphs+是一种新颖的四层因子图，包含：（1）具有机器人位姿估计的关键帧层；（2）表示墙面表面的墙体层；（3）涵盖多组墙平面的房间层；（4）汇集给定楼层内所有房间的楼层层面。上述图在实时优化中获得鲁棒且精确的机器人位姿估计与环境地图，同时构建并利用环境的高层信息。为提取这些高层信息，我们提出了利用已建图墙面和自由空间簇的新型房间与楼层分割算法。我们在多个数据集上测试了S-Graphs+，包括来自不同建筑工地的室内环境仿真与真实数据，以及真实公开的若干室内办公区域数据集。在我们的数据集平均表现中，S-Graphs+的精度比次优方法高出10.67%，同时通过更丰富的场景模型扩展了机器人的态势感知能力。此外，我们将软件以Docker文件形式公开提供。