EdgeCalib: Multi-Frame Weighted Edge Features for Automatic Targetless LiDAR-Camera Calibration

Xingchen Li, Yifan Duan, Beibei Wang, Haojie Ren, Guoliang You, Yu Sheng, Jianmin Ji, Yanyong Zhang

Abstract: In multimodal perception systems, achieving precise extrinsic calibration between LiDAR and camera is of critical importance. However, the pre-calibrated extrinsic parameters may gradually drift during operation, leading to a decrease in the accuracy of the perception system. It is challenging to address this issue using methods based on artificial targets. In this article, we introduce an edge-based approach for automatic targetless calibration of LiDARs and cameras in real-world scenarios. The edge features, which are prevalent in various environments, are used to establish reliable correspondences in images and point clouds. Specifically, we leverage the Segment Anything Model to facilitate the extraction of stable and reliable image edge features. Then a multi-frame weighting strategy is used for feature filtering while alleviating the dependence on the environment. Finally, we estimate accurate extrinsic parameters based on edge correspondence constraints. Our method achieves a mean rotation error of $0.069^{\circ }$ and a mean translation error of $\mathbf 1.037\ \text{cm}$ on the KITTI dataset, outperforming existing edge-based calibration methods and demonstrating strong robustness, accuracy, and generalization capabilities.