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.

翻译：在多模态感知系统中，实现激光雷达与相机之间的精确外参标定至关重要。以往标定方法通常需要特定目标或人工调整，既费时又成本高昂。基于特征的在线标定方法已被提出，但这些方法面临特征提取不精确、跨模态关联不可靠以及场景特定要求高等挑战。为此，我们提出一种基于边缘的方法，可在真实场景中实现激光雷达与相机的自动在线标定。边缘特征在各种环境中普遍存在，通过将图像和点云中的边缘特征对齐来确定外参参数。具体而言，使用基于SAM的方法提取稳定鲁棒的图像边缘特征，并通过多帧加权策略对点云中提取的边缘特征进行特征筛选与加权。最终，基于边缘对应约束优化得到精确的外参参数。我们在KITTI数据集和自有数据集上进行了评估。结果表明，该方法实现了0.086度的旋转精度和0.977厘米的平移精度，在精度和鲁棒性方面均优于现有基于边缘的标定方法，达到了当前最优水平。