In this paper, we propose a solution for graph-based global robot simultaneous localization and mapping (SLAM) using architectural plans. Before the start of the robot operation, the previously available architectural plan of the building is converted into our proposed architectural graph (A-Graph). When the robot starts its operation, it uses its onboard LIDAR and odometry to carry out an online SLAM relying on our situational graph (S-Graph), which includes both, a representation of the environment with multiple levels of abstractions, such as walls or rooms, and their relationships, as well as the robot poses with their associated keyframes. Our novel graph-to-graph matching method is used to relate the aforementioned S-Graph and A-Graph, which are aligned and merged, resulting in our novel informed Situational Graph (iS-Graph). Our iS-Graph not only provides graph-based global robot localization, but it extends the graph-based SLAM capabilities of the S-Graph by incorporating into it the prior knowledge of the environment existing in the architectural plan

翻译：本文提出一种利用建筑平面图的图优化全局机器人同时定位与地图构建（SLAM）方法。在机器人运行开始前，将预先获取的建筑平面图转换为我们提出的建筑图（A-Graph）。当机器人开始运行时，它利用车载激光雷达和里程计执行基于情境图（S-Graph）的在线SLAM，该图不仅包含墙面或房间等多层次抽象环境表征及其相互关系，还包含机器人位姿及其关联关键帧。我们采用新型图到图匹配方法关联上述S-Graph与A-Graph，二者经对齐与融合后形成我们提出的知情情境图（iS-Graph）。iS-Graph不仅实现基于图的全局机器人定位，还将建筑平面图中存在的环境先验知识融入其中，从而扩展了S-Graph的图优化SLAM能力。