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。

DeepMapping2: 自监督大规模 LiDAR 地图优化

DeepMapping2: Self-Supervised Large-Scale LiDAR Map Optimization

We propose DeepMapping, a novel registration framework using deep neural
networks (DNNs) as auxiliary functions to align multiple point clouds from
scratch to a globally consistent frame. We use DNNs to model the highly
non-convex mapping process that traditionally involves hand-crafted data
association, sensor pose initialization, and global refinement. Our key novelty
is that "training" these DNNs with properly defined unsupervised losses is
equivalent to solving the underlying registration problem, but less sensitive
to good initialization than ICP. Our framework contains two DNNs: a
localization network that estimates the poses for input point clouds, and a map
network that models the scene structure by estimating the occupancy status of
global coordinates. This allows us to convert the registration problem to a
binary occupancy classification, which can be solved efficiently using
gradient-based optimization. We further show that DeepMapping can be readily
extended to address the problem of Lidar SLAM by imposing geometric constraints
between consecutive point clouds. Experiments are conducted on both simulated
and real datasets. Qualitative and quantitative comparisons demonstrate that
DeepMapping often enables more robust and accurate global registration of
multiple point clouds than existing techniques. Our code is available at
this https URL

使用深度神经网络作为辅助工具，DeepMapping 提出了一种新的注册框架，可将多个点云从头开始对齐到全局一致框架中，并将注册问题转换为二进制空间分类。实验证明，相对于现有技术，DeepMapping 通常能够实现更 robust，更准确的全局点云注册。