Connecting Semantic Building Information Models and Robotics: An application to 2D LiDAR-based localization

This paper proposes a method to integrate the rich semantic data-set provided by Building Information Modeling (BIM) with robotics world models, taking as use case indoor semantic localization in a large university building. We convert a subset of semantic entities with associated geometry present in BIM models and represented in the Industry Foundation Classes (IFC) data format to a robot-specific world model representation. This representation is then stored in a spatial database from which the robot can query semantic objects in its immediate surroundings. The contribution of this work is that, from this query, the robot's feature detectors are configured and used to make explicit data associations with semantic structural objects from the BIM model that are located near the robot's current position. A graph-based approach is then used to localize the robot, incorporating the explicit map-feature associations for localization. We show that this explainable model-based approach allows a robot equipped with a 2D LiDAR and odometry to track its pose in a large indoor environment for which a BIM model is available.

Automated summary

This paper proposes a method to integrate BIM's rich semantic data-set with robot world models for indoor semantic localization. By converting semantic entities in BIM models to robot-specific world model representations and storing in a spatial database, robots can query semantic objects in their surroundings. Using a graph-based approach for robot localization, the method allows a robot equipped with 2D LiDAR and odometry to track its pose in a large indoor environment where a BIM model is available.