  Global localization is an important and widely studied problem for many
robotic applications. Place recognition approaches can be exploited to solve
this task, e.g., in the autonomous driving field. While most vision-based
approaches match an image w.r.t. an image database, global visual localization
within LiDAR-maps remains fairly unexplored, even though the path toward high
definition 3D maps, produced mainly from LiDARs, is clear. In this work we
leverage Deep Neural Network (DNN) approaches to create a shared embedding
space between images and LiDAR-maps, allowing for image to 3D-LiDAR place
recognition. We trained a 2D and a 3D DNN that create embeddings, respectively
from images and from point clouds, that are close to each other whether they
refer to the same place. An extensive experimental activity is presented to
assess the effectiveness of the approach w.r.t. different learning paradigms,
network architectures, and loss functions. All the evaluations have been
performed using the Oxford Robotcar Dataset, which encompasses a wide range of
weather and light conditions.
