In this paper, we propose a solution for legged robot localization using architectural plans. Our specific contributions towards this goal are several. Firstly, we develop a method for converting the plan of a building into what we denote as an architectural graph (A-Graph). When the robot starts moving in an environment, we assume it has no knowledge about it, and it estimates an online situational graph representation (S-Graph) of its surroundings. We develop a novel graph-to-graph matching method, in order to relate the S-Graph estimated online from the robot sensors and the A-Graph extracted from the building plans. Note the challenge in this, as the S-Graph may show a partial view of the full A-Graph, their nodes are heterogeneous and their reference frames are different. After the matching, both graphs are aligned and merged, resulting in what we denote as an informed Situational Graph (iS-Graph), with which we achieve global robot localization and exploitation of prior knowledge from the building plans. Our experiments show that our pipeline shows a higher robustness and a significantly lower pose error than several LiDAR localization baselines.

翻译：本文提出了一种利用建筑平面图实现足式机器人定位的解决方案。为实现此目标，我们作出了以下具体贡献：首先，开发了一种将建筑平面图转换为"建筑图"（A-Graph）的方法。当机器人在未知环境中运动时，我们假设其不具备任何环境先验知识，并在线估计环境中的"情境图"（S-Graph）表征。为建立机器人传感器在线估计的S-Graph与建筑平面图提取的A-Graph之间的关联，我们提出了一种新型图匹配方法。此方法的挑战在于：S-Graph可能仅呈现完整A-Graph的局部视图，两者节点类型异构且参考坐标系不同。经过图匹配后，两张图被对齐融合，形成我们称之为"增强情境图"（iS-Graph）的复合结构，从而实现全局机器人定位并利用建筑平面图的先验知识。实验表明，与多种激光雷达定位基线方法相比，本方案具有更高的鲁棒性和显著更低的位姿误差。