  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.
