  Accurate metrical localization is one of the central challenges in mobile
robotics. Many existing methods aim at localizing after building a map with the
robot. In this paper, we present a novel approach that instead uses geotagged
panoramas from the Google Street View as a source of global positioning. We
model the problem of localization as a non-linear least squares estimation in
two phases. The first estimates the 3D position of tracked feature points from
short monocular camera sequences. The second computes the rigid body
transformation between the Street View panoramas and the estimated points. The
only input of this approach is a stream of monocular camera images and odometry
estimates. We quantified the accuracy of the method by running the approach on
a robotic platform in a parking lot by using visual fiducials as ground truth.
Additionally, we applied the approach in the context of personal localization
in a real urban scenario by using data from a Google Tango tablet.
