  Globally localizing in a given map is a crucial ability for robots to perform
a wide range of autonomous navigation tasks. This paper presents OneShot - a
global localization algorithm that uses only a single 3D LiDAR scan at a time,
while outperforming approaches based on integrating a sequence of point clouds.
Our approach, which does not require the robot to move, relies on
learning-based descriptors of point cloud segments and computes the full 6
degree-of-freedom pose in a map. The segments are extracted from the current
LiDAR scan and are matched against a database using the computed descriptors.
Candidate matches are then verified with a geometric consistency test. We
additionally present a strategy to further improve the performance of the
segment descriptors by augmenting them with visual information provided by a
camera. For this purpose, a custom-tailored neural network architecture is
proposed. We demonstrate that our LiDAR-only approach outperforms a
state-of-the-art baseline on a sequence of the KITTI dataset and also evaluate
its performance on the challenging NCLT dataset. Finally, we show that fusing
in visual information boosts segment retrieval rates by up to 26% compared to
LiDAR-only description.
