ChatMap: A Wearable Platform Based on the Multi-modal Foundation Model to Augment Spatial Cognition for People with Blindness and Low Vision

Spatial cognition refers to the ability to gain knowledge about their surroundings and utilize this information to identify their location, acquire resources, and navigate their way back to familiar places. People with blindness and low vision (pBLV) face significant challenges with spatial cognition due to the reliance on visual input. Without the full range of visual cues, pBLV individuals often find it difficult to grasp a comprehensive understanding of their environment, leading to obstacles in scene recognition and precise object localization, especially in unfamiliar environments. This limitation extends to their ability to independently detect and avoid potential tripping hazards, making navigation and interaction with their environment more challenging. In this paper, we present a pioneering wearable platform tailored to enhance the spatial cognition of pBLV through the integration of multi-modal foundation model. The proposed platform integrates a wearable camera with audio module and leverages the advanced capabilities of vision language foundation model (i.e., GPT-4 and GPT-4V), for the nuanced processing of visual and textual data. Specifically, we employ vision language models to bridge the gap between visual information and the proprioception of visually impaired users, offering more intelligible guidance by aligning visual data with the natural perception of space and movement. Then we apply prompt engineering to guide the large language model to act as an assistant tailored specifically for pBLV users to produce accurate answers. Another innovation in our model is the incorporation of a chain of thought reasoning process, which enhances the accuracy and interpretability of the model, facilitating the generation of more precise responses to complex user inquiries across diverse environmental contexts. To assess the practical impact of our proposed wearable platform, we carried out a series of real-world experiments across three tasks that are commonly challenging for people with blindness and low vision: risk assessment, object localization, and scene recognition. Additionally, through an ablation study conducted on the VizWiz dataset, we rigorously assess the contribution of each individual module, substantiating the integral role in the model's overall performance.