Point cloud registration is an important task in robotics and autonomous driving to estimate the ego-motion of the vehicle. Recent advances following the coarse-to-fine manner show promising potential in point cloud registration. However, existing methods rely on good superpoint correspondences, which are hard to be obtained reliably and efficiently, thus resulting in less robust and accurate point cloud registration. In this paper, we propose a novel network, named RDMNet, to find dense point correspondences coarse-to-fine and improve final pose estimation based on such reliable correspondences. Our RDMNet uses a devised 3D-RoFormer mechanism to first extract distinctive superpoints and generates reliable superpoints matches between two point clouds. The proposed 3D-RoFormer fuses 3D position information into the transformer network, efficiently exploiting point clouds' contextual and geometric information to generate robust superpoint correspondences. RDMNet then propagates the sparse superpoints matches to dense point matches using the neighborhood information for accurate point cloud registration. We extensively evaluate our method on multiple datasets from different environments. The experimental results demonstrate that our method outperforms existing state-of-the-art approaches in all tested datasets with a strong generalization ability.

翻译：点云配准是机器人和自动驾驶领域中用于估计车辆自运动的重要任务。近年来，遵循从粗到精策略的方法在点云配准中展现出巨大潜力。然而，现有方法依赖可靠的超点对应关系，而这类对应关系难以高效且稳定地获取，导致点云配准的鲁棒性和精度不足。本文提出一种新型网络RDMNet，通过从粗到精的方式寻找密集点对应，并基于这些可靠对应关系改进最终位姿估计。RDMNet采用所设计的3D-RoFormer机制，首先提取具有辨识度的超点，并在两片点云之间生成可靠的超点匹配。该3D-RoFormer将三维位置信息融入Transformer网络，高效利用点云的上下文与几何信息以生成鲁棒的超点对应。随后，RDMNet利用邻域信息将稀疏的超点匹配传播至密集点匹配，从而实现精确的点云配准。我们在多个不同环境的数据集上对该方法进行充分评估。实验结果表明，本方法在所有测试数据集上均优于现有最先进方法，且具备强大的泛化能力。