LiDAR mapping is important yet challenging in self-driving and mobile
robotics. To tackle such a global point cloud registration problem, DeepMapping
converts the complex map estimation into a self-supervised training of simple
deep networks. Despite its broad convergence range on small datasets,
DeepMapping still cannot produce satisfactory results on large-scale datasets
with thousands of frames. This is due to the lack of loop closures and exact
cross-frame point correspondences, and the slow convergence of its global
localization network. We propose DeepMapping2 by adding two novel techniques to
address these issues: (1) organization of training batch based on map topology
from loop closing, and (2) self-supervised local-to-global point consistency
loss leveraging pairwise registration. Our experiments and ablation studies on
public datasets (KITTI, NCLT, and Nebula) demonstrate the effectiveness of our
method.

DeepMapping2 提出了两种新技术，基于地图拓扑的组织训练数据批次，以及借助成对的点云配准进行自监督局部 - 全局点云一致性损失，解决 DeepMapping 在大规模数据集中计算慢、全局定位网络收敛不足和缺少闭环和精确跨帧点对应等问题，实验表明了我们方法的有效性。