ACCURATE LOCALIZATION IN DENSE URBAN AREA USING
GOOGLE STREET VIEW IMAGES
Mahdi Salarian Andrea Manavella Rashid Ansari
Email: msalar2@uic.edu Email mana1990@hotmail.it Email: ransa@uic.edu
School of Electrical and Computer Engineering
University of Illinois at Chicago
Chicago, IL
ABSTRACT
Accurate information about the location and orientation of a
camera in mobile devices is central to the utilization of
location-based services (LBS). Most of such mobile devices
rely on GPS data but this data is subject to inaccuracy due to
imperfections in the quality of the signal provided by
satellites. This shortcoming has spurred the research into
improving the accuracy of localization. Since mobile devices
have camera, a major thrust of this research has been seeks to
acquire the local scene and apply image retrieval techniques
by querying a GPS-tagged image database to find the best
match for the acquired scene.. The techniques are however
computationally demanding and unsuitable for real-time
applications such as assistive technology for navigation by
the blind and visually impaired which motivated out work.
To overcome the high complexity of those techniques, we
investigated the use of inertial sensors as an aid in image-
retrieval-based approach. Armed with information of media
other than images, such as data from the GPS module along
with orientation sensors such as accelerometer and gyro, we
sought to limit the size of the image set to c search for the
best match. Specifically, data from the orientation sensors
along with Dilution of precision (DOP) from GPS are used to
find the angle of view and estimation of position. We present
analysis of the reduction in the image set size for the search
as well as simulations to demonstrate the effectiveness in a
fast implementation with 98% Estimated Position Error.
Index Terms— mobile device, LBS, Geotagged-images,