Mobility and safety are primary concerns for blind and visually impaired (BVI) users when navigating in unfamiliar environments. Typically, a sighted person can locate a place of interest if they are provided guidance while approaching within a few meters of the location. However, this resolution of guidance is often insufficient for blind travelers. In this thesis, we propose a crowdsourcing based micro navigation system for BVI users in both indoor and outdoor environments. To achieve this goal, our system includes three parts: crowdsourcing reports generated by volunteers using the volunteer application, landmarks validation performed by the system administrator using the admin application, and the BVI user navigation obtained through the BVI user application. In addition, we provide accessible audio navigation for indoor and outdoor environments required to deliver real time step by step landmark information to BVI users.
Crowdsourcing is enabled by the contribution of many volunteers which use the proposed volunteer application to report specific landmarks in the environment including their location, description and surrounding landmarks. These reports which are uploaded to the server database, are validated by the admin application which updates the server database and deploy BLE tags for indoor environment. The BVI user application localizes users by GPS outdoors and BLE proximity technology indoors. Using the real-time location of users and the landmark node graph we built from updated server database, this application provides the shortest route to the destination and real time “micro-navigation” information describing how to get to the next landmark’s location with corresponding distance & orientation. This information is used ix to make users well aware of where they are, and guide users to their chosen destination within a cane’s distance.
This application will improve the confidence and safety of BVI users by enabling them to explore and get navigation in both indoor and outdoor environments.
| Identifer | oai:union.ndltd.org:UMASS/oai:scholarworks.umass.edu:masters_theses_2-1734 |
| Date | 25 October 2018 |
| Creators | Shi, Quan |
| Publisher | ScholarWorks@UMass Amherst |
| Source Sets | University of Massachusetts, Amherst |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Masters Theses |
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