We focus on the problem of LiDAR point cloud based loop detection (or Finding) and closure (LDC) in a multi-agent setting. State-of-the-art (SOTA) techniques directly generate learned embeddings of a given point cloud, require large data transfers, and are not robust to wide variations in 6 Degrees-of-Freedom (DOF) viewpoint. Moreover, absence of strong priors in an unstructured point cloud leads to highly inaccurate LDC. In this original approach, we propose independent roll and pitch canonicalization of the point clouds using a common dominant ground plane. Discretization of the canonicalized point cloud along the axis perpendicular to the ground plane leads to an image similar to Digital Elevation Maps (DEMs), which exposes strong spatial priors in the scene. Our experiments show that LDC based on learnt embeddings of such DEMs is not only data efficient but also significantly more robust, and generalizable than the current SOTA. We report significant performance gain in terms of Average Precision for loop detection and absolute translation/rotation error for relative pose estimation (or loop closure) on Kitti, GPR and Oxford Robot Car over multiple SOTA LDC methods. Our encoder technique allows to compress the original point cloud by over 830 times. To further test the robustness of our technique we create and opensource a custom dataset called Lidar-UrbanFly Dataset (LUF) which consists of point clouds obtained from a LiDAR mounted on a quadrotor.

翻译：我们聚焦于多智能体场景下基于LiDAR点云的回环检测（或查找）与闭合问题。现有最优技术直接生成给定点云的学得嵌入表示，需大量数据传输，且对六自由度视角的大范围变化缺乏鲁棒性。此外，非结构化点云中缺乏强先验信息会导致回环检测与闭合高度不准确。在本原创方法中，我们提出利用公共主导地平面实现点云的独立翻滚角与俯仰角规范化。沿地平面法线方向对规范化点云进行离散化处理，可生成类似数字高程图的图像，从而暴露场景中的强空间先验。实验表明，基于此类数字高程图学得嵌入的回环检测与闭合技术不仅数据高效，且显著提升鲁棒性与泛化能力，性能超越当前最优方法。我们在KITTI、GPR及Oxford RobotCar数据集上，针对回环检测的平均精度和相对位姿估计（即回环闭合）的绝对平移/旋转误差，相较于多种主流回环检测与闭合方法取得显著提升。提出的编码器技术可实现原始点云超830倍的压缩。为验证技术鲁棒性，我们创建并开源了名为Lidar-UrbanFly数据集（LUF）的定制数据集，该数据集包含四旋翼无人机搭载的LiDAR采集的点云数据。