Global point cloud registration is essential in many robotics tasks like loop
closing and relocalization. Unfortunately, the registration often suffers from
the low overlap between point clouds, a frequent occurrence in practical
applications due to occlusion and viewpoint change. In this paper, we propose a
graph-theoretic framework to address the problem of global point cloud
registration with low overlap. To this end, we construct a consistency graph to
facilitate robust data association and employ graduated non-convexity (GNC) for
reliable pose estimation, following the state-of-the-art (SoTA) methods.
Unlike previous approaches, we use semantic cues to scale down the dense
point clouds, thus reducing the problem size. Moreover, we address the
ambiguity arising from the consistency threshold by constructing a pyramid
graph with multi-level consistency thresholds. Then we propose a cascaded
gradient ascend method to solve the resulting densest clique problem and obtain
multiple pose candidates for every consistency threshold. Finally, fast
geometric verification is employed to select the optimal estimation from
multiple pose candidates. Our experiments, conducted on a self-collected indoor
dataset and the public KITTI dataset, demonstrate that our method achieves the
highest success rate despite the low overlap of point clouds and low semantic
quality. We have open-sourced our code
this https URL for this project.

提出了一种应用图论框架的全局点云配准方法，利用语义线索缩小点云规模解决点云之间重叠度低的问题，通过构建金字塔图进行多级一致性阈值匹配，用级联式梯度上升方法解决最密团问题，得到多个位姿候选项，并通过快速几何验证选择最佳估计。实验证明，尽管点云之间重叠度低且语义质量低，该方法实现了最高的成功率。

金字塔语义图全局点云配准方法在低重叠度下的应用

Pyramid Semantic Graph-based Global Point Cloud Registration with Low  Overlap

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。