  LiDAR based place recognition is popular for loop closure detection and
re-localization. In recent years, deep learning brings improvements to place
recognition by learnable feature extraction. However, these methods degenerate
when the robot re-visits previous places with large perspective difference. To
address the challenge, we propose DeepRING to learn the roto-translation
invariant representation from LiDAR scan, so that robot visits the same place
with different perspective can have similar representations. There are two keys
in DeepRING: the feature is extracted from sinogram, and the feature is
aggregated by magnitude spectrum. The two steps keeps the final representation
with both discrimination and roto-translation invariance. Moreover, we state
the place recognition as a one-shot learning problem with each place being a
class, leveraging relation learning to build representation similarity.
Substantial experiments are carried out on public datasets, validating the
effectiveness of each proposed component, and showing that DeepRING outperforms
the comparative methods, especially in dataset level generalization.
