Turn-by-Turn Indoor Navigation for the Visually Impaired

Navigating indoor environments presents significant challenges for visually impaired individuals due to complex layouts and the absence of GPS signals. This paper introduces a novel system that provides turn-by-turn navigation inside buildings using only a smartphone equipped with a camera, leveraging multimodal models, deep learning algorithms, and large language models (LLMs). The smartphone's camera captures real-time images of the surroundings, which are then sent to a nearby Raspberry Pi capable of running on-device LLM models, multimodal models, and deep learning algorithms to detect and recognize architectural features, signage, and obstacles. The interpreted visual data is then translated into natural language instructions by an LLM running on the Raspberry Pi, which is sent back to the user, offering intuitive and context-aware guidance via audio prompts. This solution requires minimal workload on the user's device, preventing it from being overloaded and offering compatibility with all types of devices, including those incapable of running AI models. This approach enables the client to not only run advanced models but also ensure that the training data and other information do not leave the building. Preliminary evaluations demonstrate the system's effectiveness in accurately guiding users through complex indoor spaces, highlighting its potential for widespread application