Robust LiDAR-based localization in architectural floor plans

Modern automation demands mobile robots to be robustly localized in complex scenarios. Current localization systems typically use maps that require to be built and interpreted by experienced operators, increasing deployment costs as well as reducing the adaptability of robots to rearrangements in the environment. In contrast, architectural floor plans can be easily understood by non-expert users and typically represent only the non-rearrangeable parts of buildings. In this paper we propose a system for robot localization in architectural CAD drawings. Our method employs a simultaneous localization and mapping approach to online augment the floor plan with a map represented as a pose-graph with LiDAR measurements. Whenever the environment is accurately mapped in the vicinity of the robot, we use the graph to perform relative localization. We thoroughly evaluate our system in challenging real-world scenarios. Experiments demonstrate that our method is able to robustly track the robot pose even when the floor plan shows major discrepancies from the real-world. We show that our system achieves sub-centimeter accuracy and is suitable for real-time application.