Accurate localization in dense urban area using Google street view images

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 a camera, a major thrust of this research has been directed at acquiring the local scene and applying 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. 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 number of candidate images that should be considered for finding the best match. Specifically, data from the orientation sensors (heading) along with Dilution of Precision (DOP) from GPS are used to find the angle of view and the estimate of location. 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 acceptable location error.