NDLI: Self-Localization at Street Intersections

Content Provider	IEEE Xplore Digital Library
Author	Fusco, G. Huiying Shen Coughlan, J.M.
Copyright Year	2014
Description	Author affiliation: Smith-Kettlewell Eye Res. Inst., San Francisco, CA, USA (Fusco, G.; Huiying Shen; Coughlan, J.M.)
Abstract	There is growing interest among smartphone users in the ability to determine their precise location in their environment for a variety of applications related to way finding, travel and shopping. While GPS provides valuable self-localization estimates, its accuracy is limited to approximately 10 meters in most urban locations. This paper focuses on the self-localization needs of blind or visually impaired travelers, who are faced with the challenge of negotiating street intersections. These travelers need more precise self-localization to help them align themselves properly to crosswalks, signal lights and other features such as walk light pushbuttons. We demonstrate a novel computer vision-based localization approach that is tailored to the street intersection domain. Unlike most work on computer vision-based localization techniques, which typically assume the presence of detailed, high-quality 3D models of urban environments, our technique harnesses the availability of simple, ubiquitous satellite imagery (e.g., Google Maps) to create simple maps of each intersection. Not only does this technique scale naturally to the great majority of street intersections in urban areas, but it has the added advantage of incorporating the specific metric information that blind or visually impaired travelers need, namely, the locations of intersection features such as crosswalks. Key to our approach is the integration of IMU (inertial measurement unit) information with geometric information obtained from image panorama stitchings. Finally, we evaluate the localization performance of our algorithm on a dataset of intersection panoramas, demonstrating the feasibility of our approach.
Starting Page	40
Ending Page	47
File Size	855303
Page Count	8
File Format	PDF
e-ISBN	9781479943371
DOI	10.1109/CRV.2014.14
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-06
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computers Transforms assistive technology blindness and low vision Global Positioning System Accuracy Three-dimensional displays image stitching Magnetic resonance imaging mobile vision self-localization Cameras IMU (inertial measurement unit)
Content Type	Text
Resource Type	Article

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