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
In multimodal perception systems, achieving precise extrinsic calibration
between LiDAR and camera is of critical importance. Previous calibration
methods often required specific targets or manual adjustments, making them both
labor-intensive and costly. Online calibration methods based on features have
been proposed, but these methods encounter challenges such as imprecise feature
extraction, unreliable cross-modality associations, and high scene-specific
requirements. To address this, we introduce an edge-based approach for
automatic online calibration of LiDAR and cameras in real-world scenarios. The
edge features, which are prevalent in various environments, are aligned in both
images and point clouds to determine the extrinsic parameters. Specifically,
stable and robust image edge features are extracted using a SAM-based method
and the edge features extracted from the point cloud are weighted through a
multi-frame weighting strategy for feature filtering. Finally, accurate
extrinsic parameters are optimized based on edge correspondence constraints. We
conducted evaluations on both the KITTI dataset and our dataset. The results
show a state-of-the-art rotation accuracy of 0.086{\deg} and a translation
accuracy of 0.977 cm, outperforming existing edge-based calibration methods in
both precision and robustness.