Text2Map: From Navigational Instructions to Graph-Based Indoor Map Representations Using LLMs

In spatial navigation, the shift from manual cartography to digital map representations has revolutionized how we interact with and comprehend outdoor and indoor environments. While digital mapping has substantially advanced outdoor navigation with robust techniques like satellite imagery and sophisticated data labeling, the full potential of indoor digital mapping remains untapped. Accurate indoor mapping promises to enhance the operational efficiency of mobile robots, improving their ability to interact with human environments, and bolstering emergency response capabilities. However, its realization is impeded by the complexity of current methods and the need for heavy manual labor, expert knowledge, and specialized equipment. To address these challenges, we introduce Text2Map – a novel methodology that harnesses natural language navigational instructions, the power of off-the-shelf Large Language Models (LLMs), and Few-shot Learning, to create graph-based digital maps of indoor spaces. This approach simplifies the mapping process for widespread use, leveraging crowd-sourceable ubiquitous navigation instructions as a data source without requiring specialized map data formats or hardware. Our paper presents the Text2Map system architecture, details the creation of the first dedicated dataset, and evaluates the system’s efficacy, highlighting the substantial potential and scalability of our approach. Text2Map achieves a Graph-Edit-Distance (GED) ranging from 0.5X to 2X the total number of regions in a building and an Edge Similarity score between 0.87 and 0.9. These results highlight the precision, robustness, and effectiveness of our methodology. Our work paves the way for a more accessible and streamlined approach to indoor digital mapping, setting the stage for broader adoption in human and mobile robot navigation applications.