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 https://github.com/HKUST-Aerial-Robotics/Pagor for this project.

翻译：全局点云配准在机器人领域的许多任务中至关重要，例如环路闭合与重定位。然而，由于实际应用中遮挡和视角变化导致点云重叠度较低，配准过程常面临挑战。本文提出一种基于图论的框架，以解决低重叠度下的全局点云配准问题。为此，我们遵循现有最优方法构建一致性图以增强鲁棒数据关联，并采用渐进非凸性方法实现可靠位姿估计。与先前方法不同，我们利用语义线索对稠密点云进行降维，从而缩小问题规模。此外，针对一致性阈值引发的歧义，我们构建了具有多层级一致性阈值的金字塔图，并提出级联梯度上升方法求解由此产生的最大团问题，为每个一致性阈值生成多个位姿候选。最终通过快速几何验证从多个候选位姿中选取最优估计。我们在自采室内数据集和公开KITTI数据集上的实验表明，即使在点云重叠度低且语义质量不佳的情况下，本方法仍取得了最高成功率。本项目的代码已在https://github.com/HKUST-Aerial-Robotics/Pagor开源。