  An efficient method is proposed for refining GPS-acquired location
coordinates in urban areas using camera images, Google Street View (GSV) and
sensor parameters. The main goal is to compensate for GPS location imprecision
in dense area of cities due to proximity to walls and buildings. Avail-able
methods for better localization often use visual information by using query
images acquired with camera-equipped mobile devices and applying image
retrieval techniques to find the closest match in a GPS-referenced image data
set. The search areas required for reliable search are about 1-2 sq. Km and the
accuracy is typically 25-100 meters. Here we describe a method based on image
retrieval where a reliable search can be confined to areas of 0.01 sq. Km and
the accuracy in our experiments is less than 10 meters. To test our procedure
we created a database by acquiring all Google Street View images close to what
is seen by a pedestrian in a large region of downtown Chicago and saved all
coordinates and orientation data to be used for confining our search region.
Prior knowledge from approximate position of query image is leveraged to
address complexity and accuracy issues of our search in a large scale
geo-tagged data set. One key aspect that differentiates our work is that it
utilizes the sensor information of GPS SOS and the camera orientation in
improving localization. Finally we demonstrate retrieval-based technique are
less accurate in sparse open areas compared with purely GPS measurement. The
effectiveness of our approach is discussed in detail and experimental results
show improved performance when compared with regular approaches.
