RLCNet: A Novel Deep Feature-Matching-Based Method for Online Target-Free Radar-LiDAR Calibration

While millimeter-wave radars are widely used in robotics and autonomous driving, extrinsic calibration with other sensors remains challenging due to the sparsity and uncertainty of radar point clouds. In this paper, we propose a novel deep feature-matching-based online extrinsic calibration approach for a 4D millimeter-wave radar and 3D LiDAR system. We formulate the calibration problem as a crossmodal point cloud registration task, initiating with keypointlevel matching followed by dense matching refinement. Efficient yet powerful neural networks are employed to extract prior keypoint matches, which are then expanded to surrounding regions, establishing dense point correspondences. Our approach effectively leverages the majority of the information from millimeter-wave radar, mitigating the impact of radar point cloud sparsity. We evaluate our approach on two datasets, and experimental results demonstrate that it outperforms state-of-the-art baseline methods and achieves an average improvement of 66.96% in calibration success rate, while reducing translational error and rotational error by 23.84% and 30.31%, respectively. Our implementation will be made open-source at https://github.com/nubot-nudt/RLCNet.