A Computer Vision-Based Indoor Wayfinding Tool

The ability to navigate safely and confidently is a fundamental requirement for independent travel and access to many settings such as work, school, shopping, transit and healthcare. Navigation is particularly challenging for people with visual impairments, who have limited ability to see signs, landmarks or maps posted in the environment. While a variety of GPS-based wayfinding aids are available for this population, the lack of GPS access indoors has meant that very few alternative sources of wayfinding information are available in this setting.

We are developing a smartphone-based indoor navigation assistance system that uses computer vision to estimate the user's location in an indoor environment and guide him/her to a desired destination. The advantages of our approach are (a) it runs on a standard smartphone and requires no new physical infrastructure, just a digital 2D map of the indoor environment that includes the locations of signs in it; and (b) it allows the user to walk freely without having to actively search for signs with the smartphone (which is challenging for people with severe visual impairments).

A four-year grant from NIH/NEI (R01EY029033) was received in April 2018 to fund this project!

Tabs

Conference Papers
Biggs, B., Coughlan, J., & Coppin, P.. (2019). Design and Evaluation of an Audio Game-Inspired Auditory Map Interface. In The 25th International Conference on Auditory Display (ICAD 2019). Northumbria University, Newcastle-upon-Tyne, UK. (Original work published 2019)
Fusco, G., & Coughlan, J.. (2018). Indoor Localization using Computer Vision and Visual-Inertial Odometry. In International Conference on Computers Helping People with Special Needs (ICCHP '18). Linz, Austria. (Original work published 2018)
Rituerto, A., Fusco, G., & Coughlan, J.. (2016). Towards a Sign-Based Indoor Navigation System for People with Visual Impairments. In 18th International ACM SIGACCESS Conference on Computers and Accessibility. Reno, NV: ACM.
  • L to R: Huiying Shen, Ali Cheraghi, Brandon Biggs, James Coughlan, Charity Pitcher-Cooper, Giovanni Fusco

    Coughlan Lab

    The goal of our laboratory is to develop and test assistive technology for blind and visually impaired persons that is enabled by computer vision and other sensor technologies.

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