Abstract-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.
REF used the mobile version of the Tango project for metrical localization purposes based on the Google Street View.
Metric localization using Google Street View
2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Engineering Computer Science