  We propose an extension to the segment-based global localization method for
LiDAR SLAM using descriptors learned considering the visual context of the
segments. A new architecture of the deep neural network is presented that
learns the visual context acquired from synthetic LiDAR intensity images. This
approach allows a single multi-beam LiDAR to produce rich and highly
descriptive location signatures. The method is tested on two public datasets,
demonstrating an improved descriptiveness of the new descriptors, and more
reliable loop closure detection in SLAM. Attention analysis of the network is
used to show the importance of focusing on the broader context rather than only
on the 3-D segment.
