Physico-Geospatial Grounded Scene Interpretation for Mobile Robotics
Recent advancements in deep learning allow robotic agents to interact with dynamic and unstructured environments. Of special interest is the integration of physico-geospatial world knowledge into such systems, either by using physics-aware machine learning models, knowledge graphs to model relationships or spatio-temporal and logical reasoning. In the present work, we introduce an approach to augment the output of pre-trained, unmodified VLMs used for scene interpretation by integrating semantic descriptions, OpenStreetMap building data and street information with positional, temporal and metric information obtained from our sensory systems, fusing this information using LLMs. We apply th...
| Authors | Nicolas Schuler, Janik Kurtz, Lea Dewald et al. |
|---|---|
| Primary category | cs.CV |
| Published | 6 September 2026 |
| Last updated at source | 6 September 2026 |
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