Navigation and Interaction for Blind Users via a Cognitive Architecture

Abstract

Navigating new indoor spaces and interacting with the environment presents many challenges for people who are blind or have low vision (BLV). To address these challenges, we prototyped a smartphone-based conversational assistant that helps BLV people navigate and interact with their environment. The prototype utilizes a cognitive architecture to integrate three different technologies: (i) augmented-reality spatial anchors for high-precision localization and access to static information about the environment; (ii) real-time object/people detection for information about the environment and obstacle avoidance; and (iii) a conversational agent}that uses large language models (LLMs) for information extraction, conversational interaction, and turn-by-turn navigation. We assess the impact of different technologies on human performance by measuring user task time and errors. We found that conversational interaction holistically integrates the different technologies to deliver a better user experience while significantly reducing task completion time.