  In this paper, we investigate visual-based camera localization with neural
networks for robotics and autonomous vehicles applications. Our solution is a
CNN-based algorithm which predicts camera pose (3D translation and 3D rotation)
directly from a single image. It also provides an uncertainty estimate of the
pose. Pose and uncertainty are learned together with a single loss function.
Furthermore, we propose a new fully convolutional architecture, named
CoordiNet, designed to embed some of the scene geometry.
  Our framework outperforms comparable methods on the largest available
benchmark, the Oxford RobotCar dataset, with an average error of 8 meters where
previous best was 19 meters. We have also investigated the performance of our
method on large scenes for real time (18 fps) vehicle localization. In this
setup, structure-based methods require a large database, and we show that our
proposal is a reliable alternative, achieving 29cm median error in a 1.9km loop
in a busy urban area.
