Generation of skill-specific maps from graph world models for robotic systems
<|reference_start|>Generation of skill-specific maps from graph world models for robotic systems: 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.<|reference_end|>
@article{de vos2024generation,
    title={Generation of skill-specific maps from graph world models for robotic
  systems},
    author={Koen de Vos, Gijs van den Brandt, Jordy Senden, Pieter Pauwels, Rene
  van de Molengraft and Elena Torta},
    journal={arXiv preprint arXiv:2402.18174},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.18174},
    primaryClass={cs.RO}
}