  Global localization is essential for robot navigation, of which the first
step is to retrieve a query from the map database. This problem is called place
recognition. In recent years, LiDAR scan based place recognition has drawn
attention as it is robust against the appearance change. In this paper, we
propose a LiDAR-based place recognition method, named Differentiable Scan
Context with Orientation (DiSCO), which simultaneously finds the scan at a
similar place and estimates their relative orientation. The orientation can
further be used as the initial value for the down-stream local optimal metric
pose estimation, improving the pose estimation especially when a large
orientation between the current scan and retrieved scan exists. Our key idea is
to transform the feature into the frequency domain. We utilize the magnitude of
the spectrum as the place signature, which is theoretically rotation-invariant.
In addition, based on the differentiable phase correlation, we can efficiently
estimate the global optimal relative orientation using the spectrum. With such
structural constraints, the network can be learned in an end-to-end manner, and
the backbone is fully shared by the two tasks, achieving interpretability and
light weight. Finally, DiSCO is validated on three datasets with long-term
outdoor conditions, showing better performance than the compared methods.
