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.

翻译：本文提出一种简单、有效且高效的拓扑回环检测管线——轮廓上下文（Contour Context），并辅以精确的3自由度度量姿态估计，专为城市自动驾驶场景设计。我们将3D激光雷达点云投影生成的笛卡尔鸟瞰图（BEV）解释为结构的层次化分布。为恢复BEV中的高程信息，我们在不同高度对其切片，每个高度层中的连通像素形成轮廓。每个轮廓通过抽象信息参数化，例如像素计数、中心位置、协方差和平均高程。两幅BEV图像的相似性通过序贯的离散与连续步骤计算：第一步考虑由特定局部区域的轮廓构成的图状星座的几何一致性，第二步将大部分轮廓建模为2.5D高斯混合模型，用于计算相关性并在连续空间中优化相对变换。我们设计了一个检索键以加速对由分层KD树索引的数据库的搜索。通过在公开数据集上与近期工作的比较，验证了本方法的有效性。