# Generation of skill-specific maps from graph world models for robotic systems

arXiv: https://arxiv.org/abs/2402.18174 · HTML: https://mlsanity.com/paper/2402.18174/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in World models for navigation, one of 11 directions in World models.
- Builds on 1 of its 30 references that are in the map.
- First posted on arXiv 28 Feb 2024.

## Placed in

- World models / [World models for navigation](https://mlsanity.com/wm/world-models-for-navigation/)

## Builds on (most cited first, 1 of 1)

- [Semantic localization on BIM-generated maps using a 3D LiDAR sensor](https://mlsanity.com/paper/2205.00816/) (2022)

## Abstract (arXiv)

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.