RDMNet: Reliable Dense Matching Based Point Cloud Registration for Autonomous Driving
This addresses a critical bottleneck in ego-motion estimation for autonomous vehicles, offering improved robustness and accuracy, though it appears incremental as it builds on coarse-to-fine approaches.
The paper tackles the problem of unreliable superpoint correspondences in point cloud registration for autonomous driving, proposing RDMNet which uses a 3D-RoFormer mechanism to generate robust matches and outperforms state-of-the-art methods across multiple datasets.
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.