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Metric Localization using Google Street View
Pratik Agarwal, Wolfram Burgard, Luciano Spinello
Proc. of the IEEE Int. Conf. on Intelligent Robots and Systems (IROS)
2015
agarwal15iros.pdf
Notes:
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.
BibTeX:
@inproceedings{agarwal15iros,
author = {Pratik Agarwal and Wolfram Burgard and Luciano Spinello},
title = {Metric Localization using Google Street View},
booktitle = {Proc.~of the IEEE Int.~Conf.~on Intelligent Robots and Systems (IROS)},
year = 2015,
url = {http://ais.informatik.uni-freiburg.de/publications/papers/agarwal15iros.pdf},
address = {Hamburg, Germany}
}
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