With the increase in the availability of Building Information Models (BIM) and (semi-) automatic tools to generate BIM from point clouds, we propose a world model architecture and algorithms to allow the use of the semantic and geometric knowledge encoded within these models to generate maps for robot localization and navigation. When heterogeneous robots are deployed within an environment, maps obtained from classical SLAM approaches might not be shared between all agents within a team of robots, e.g. due to a mismatch in sensor type, or a difference in physical robot dimensions. Our approach extracts the 3D geometry and semantic description of building elements (e.g. material, element type, color) from BIM, and represents this knowledge in a graph. Based on queries on the graph and knowledge of the skills of the robot, we can generate skill-specific maps that can be used during the execution of localization or navigation tasks. The approach is validated with data from complex build environments and integrated into existing navigation frameworks.

翻译：随着建筑信息模型（BIM）以及从点云生成BIM的（半）自动化工具的日益普及，我们提出了一种世界模型架构和算法，使得能够利用这些模型中编码的语义和几何知识，生成用于机器人定位与导航的地图。当异构机器人在同一环境中部署时，通过经典SLAM方法获得的地图可能无法在机器人团队中的所有智能体之间共享，例如由于传感器类型不匹配或机器人物理尺寸差异。我们的方法从BIM中提取建筑元素的三维几何与语义描述（如材质、元素类型、颜色），并将这些知识以图结构表示。基于对图的查询以及机器人技能知识，我们可以生成技能特定的地图，用于执行定位或导航任务。该方法通过复杂建筑环境数据进行了验证，并集成到现有导航框架中。