Deep learning-based feature matching has shown great superiority for point cloud registration in the absence of pose priors. Although coarse-to-fine matching approaches are prevalent, the coarse matching of existing methods is typically sparse and loose without consideration of geometric consistency, which makes the subsequent fine matching rely on ineffective optimal transport and hypothesis-and-selection methods for consistency. Therefore, these methods are neither efficient nor scalable for real-time applications such as odometry in robotics. To address these issues, we design a consistency-aware spot-guided Transformer (CAST), which incorporates a spot-guided cross-attention module to avoid interfering with irrelevant areas, and a consistency-aware self-attention module to enhance matching capabilities with geometrically consistent correspondences. Furthermore, a lightweight fine matching module for both sparse keypoints and dense features can estimate the transformation accurately. Extensive experiments on both outdoor LiDAR point cloud datasets and indoor RGBD point cloud datasets demonstrate that our method achieves state-of-the-art accuracy, efficiency, and robustness.

本文研究了点云配准中的几何一致性问题，提出了一种一致性感知的点导向变换器（CAST），该方法通过点导向的交叉注意力模块和一致性感知的自注意力模块来提升匹配能力，使得匹配结果更加几何一致。实验表明，CAST在准确性、效率和鲁棒性方面均达到最先进水平，对实时应用如机器人里程计具有重要影响。

一致性感知的点导向变换器用于多样化和分层的点云配准