  Place recognition is a challenging problem in mobile robotics, especially in
unstructured environments or under viewpoint and illumination changes. Most
LiDAR-based methods rely on geometrical features to overcome such challenges,
as generally scene geometry is invariant to these changes, but tend to affect
camera-based solutions significantly. Compared to cameras, however, LiDARs lack
the strong and descriptive appearance information that imaging can provide.
  To combine the benefits of geometry and appearance, we propose coupling the
conventional geometric information from the LiDAR with its calibrated intensity
return. This strategy extracts extremely useful information in the form of a
new descriptor design, coined ISHOT, outperforming popular state-of-art
geometric-only descriptors by significant margin in our local descriptor
evaluation. To complete the framework, we furthermore develop a probabilistic
keypoint voting place recognition algorithm, leveraging the new descriptor and
yielding sublinear place recognition performance. The efficacy of our approach
is validated in challenging global localization experiments in large-scale
built-up and unstructured environments.
