Embedded Navigation and Classification System for Assisting Visually Impaired People

Abstract

Loss of vision has a large detrimental impact on a person's mobility. Every day, visually impaired people (VIPs) face various challenges just to get around in the most diverse environments. Technological solutions, called Electronic Travel Aids, help a VIP with these challenges, giving greater confidence in the task of getting around in unfamiliar surroundings. Thus, this article presents an embedded navigation and classification system for helping VIPs indoors. Using stereo vision, the system is able to detect obstacles and choose safe ways for the VIP to walk around without colliding. A convolutional neural network using a graphics processing unit (GPU) classifies the obstacles. Acoustic feedback is transmitted to the VIP. The article also features a wearable prototype, to which the system hardware is docked for use. Using the system, the prototype could detect and classify obstacles in real time defining free paths, all with battery autonomy of about 6 hours.