RoCalib: Large-Scale Autonomous Geo-Calibration for Roadside Lidar With High-Definition Map

To address the challenges of low overlap and viewing direction differences in large-scale roadside LiDAR calibration, this paper proposes RoCalib, a novel automatic roadside LiDAR calibration method based on the high-definition (HD) map. This method enables geographic registration of the LiDAR without any specific target and improves the efficiency and safety of the large-scale roadside facility calibration and maintenance. First, a novel virtual reprojection model is designed to construct a virtual mapping from the HD map to LiDAR, reducing representation differences. Based on this, a universal spatial context descriptor is introduced, applicable to various LiDAR systems, facilitating rapid retrieval of LiDAR positions within the HD map. Finally, based on the multi-feature optimization method considering the road structure, the fine registration and parameter calibration of the roadside LiDAR and the HD map are completed. The proposed framework is validated on simulated, public, and self-collected datasets, demonstrating that this method can automatically and accurately achieve multi-LiDAR geographic calibration, yielding superior performance.