Real-time registration of partially overlapping point clouds has emerging
applications in cooperative perception for autonomous vehicles and multi-agent
SLAM. The relative translation between point clouds in these applications is
higher than in traditional SLAM and odometry applications, which challenges the
identification of correspondences and a successful registration. In this paper,
we propose a novel registration method for partially overlapping point clouds
where correspondences are learned using an efficient point-wise feature
encoder, and refined using a graph-based attention network. This attention
network exploits geometrical relationships between key points to improve the
matching in point clouds with low overlap. At inference time, the relative pose
transformation is obtained by robustly fitting the correspondences through
sample consensus. The evaluation is performed on the KITTI dataset and a novel
synthetic dataset including low-overlapping point clouds with displacements of
up to 30m. The proposed method achieves on-par performance with
state-of-the-art methods on the KITTI dataset, and outperforms existing methods
for low overlapping point clouds. Additionally, the proposed method achieves
significantly faster inference times, as low as 410ms, between 5 and 35 times
faster than competing methods. Our code and dataset are available at
this https URL

该研究提出了一种新的部分重叠点云的实时配准方法，利用点云的特征编码器和基于图的注意力网络来学习和优化对应关系，实现了与现有方法相当的性能和更快的推理速度。