Graph-based Global Robot Localization Informing Situational Graphs with Architectural Graphs

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.