  This paper proposes \textit{Contour Context}, a simple, effective, and
efficient topological loop closure detection pipeline with accurate 3-DoF
metric pose estimation, targeting the urban utonomous driving scenario. We
interpret the Cartesian birds' eye view (BEV) image projected from 3D LiDAR
points as layered distribution of structures. To recover elevation information
from BEVs, we slice them at different heights, and connected pixels at each
level will form contours. Each contour is parameterized by abstract
information, e.g., pixel count, center position, covariance, and mean height.
The similarity of two BEVs is calculated in sequential discrete and continuous
steps. The first step considers the geometric consensus of graph-like
constellations formed by contours in particular localities. The second step
models the majority of contours as a 2.5D Gaussian mixture model, which is used
to calculate correlation and optimize relative transform in continuous space. A
retrieval key is designed to accelerate the search of a database indexed by
layered KD-trees. We validate the efficacy of our method by comparing it with
recent works on public datasets.
