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.

本文提出了一种使用 Google Street View 中地理标记全景作为全局定位源的新方法，通过追踪短单眼相机序列中的特征点，估计 3D 位置，然后计算 Street View 全景图与估计点之间的刚体变换，进而实现了机器人自动测距。