  Place recognition plays a crucial role in re-localization and loop closure
detection tasks for robots and vehicles. This paper seeks a well-defined global
descriptor for LiDAR-based place recognition. Compared to local descriptors,
global descriptors show remarkable performance in urban road scenes but are
usually viewpoint-dependent. To this end, we propose a simple yet robust global
descriptor dubbed FreSCo that decomposes the viewpoint difference during
revisit and achieves both translation and rotation invariance by leveraging
Fourier Transform and circular shift technique. Besides, a fast two-stage pose
estimation method is proposed to estimate the relative pose after place
retrieval by utilizing the compact 2D point clouds extracted from the original
data. Experiments show that FreSCo exhibited superior performance than
contemporaneous methods on sequences of different scenes from multiple
datasets. Code will be publicly available at https://github.com/soytony/FreSCo.
