@inproceedings{Brubaker2013CVPR,
  title = {Lost! Leveraging the Crowd for Probabilistic Visual Self-Localization},
  aword_paper = {CVPR13 Best Paper Runner-Up},
  booktitle = {IEEE Conf. on Computer Vision and Pattern Recognition (CVPR 2013)},
  abstract = {In this paper we propose an affordable solution to self-
  localization, which utilizes visual odometry and road maps
  as the only inputs. To this end, we present a probabilis-
  tic model as well as an efficient approximate inference al-
  gorithm, which is able to utilize distributed computation
  to meet the real-time requirements of autonomous systems.
  Because of the probabilistic nature of the model we are
  able to cope with uncertainty due to noisy visual odometry
  and inherent ambiguities in the map (
  e.g
  ., in a Manhattan
  world). By exploiting freely available, community devel-
  oped maps and visual odometry measurements, we are able
  to localize a vehicle up to 3m after only a few seconds of
  driving on maps which contain more than 2,150km of driv-
  able roads.},
  pages = {3057-3064},
  publisher = {IEEE},
  address = {Portland, OR},
  month = jun,
  year = {2013},
  slug = {brubaker2013cvpr},
  author = {Brubaker, Marcus A. and Geiger, Andreas and Urtasun, Raquel},
  month_numeric = {6}
}