LLM-Guided Indoor Navigation with Multimodal Map Understanding
This addresses indoor navigation challenges for users in complex environments, though it appears incremental as it applies an existing LLM to a new domain.
The paper tackles indoor navigation by using ChatGPT to generate context-aware navigation instructions from indoor map images, achieving an average of 86.59% correct indications and up to 97.14% accuracy.
Indoor navigation presents unique challenges due to complex layouts and the unavailability of GNSS signals. Existing solutions often struggle with contextual adaptation, and typically require dedicated hardware. In this work, we explore the potential of a Large Language Model (LLM), i.e., ChatGPT, to generate natural, context-aware navigation instructions from indoor map images. We design and evaluate test cases across different real-world environments, analyzing the effectiveness of LLMs in interpreting spatial layouts, handling user constraints, and planning efficient routes. Our findings demonstrate the potential of LLMs for supporting personalized indoor navigation, with an average of 86.59% correct indications and a maximum of 97.14%. The proposed system achieves high accuracy and reasoning performance. These results have key implications for AI-driven navigation and assistive technologies.