We present LV-Calib, a calibration framework for LiDAR-camera extrinsic estimation and LiDAR boundary-response calibration using a printable planar target. The target serves as a shared observation carrier: visual fiducials provide indexed image measurements, while circular reflectivity boundaries provide LiDAR-observable structural feature points. Instead of directly fitting boundary points as ideal geometric contours, LV-Calib automatically crops background points, estimates the target plane, and iteratively refines accurate LiDAR-side 3-D feature points from intensity and geometric constraints. The refinement explicitly handles the broadened and distorted transition band induced by finite beam footprint and mixed-intensity returns around black-white reflectivity discontinuities. Given these refined LiDAR features, we formulate a weighted reprojection-consistent extrinsic optimization with LiDAR feature alignment, where image observations are kept in the reprojection domain and LiDAR feature residuals are weighted by refinement confidence. Finally, using the estimated extrinsic and the extracted transition band, LV-Calib calibrates the LiDAR boundary response by estimating pitch-yaw-range residual statistics of boundary-overlap samples. Experiments on printed-board calibration data demonstrate sub-pixel reprojection accuracy, millimeter-level LiDAR feature consistency, and improved odometry performance. Code and calibration data will be released for reproducible evaluation.

翻译：我们提出LV-Calib，一种利用可打印平面靶标实现激光雷达-相机外参估计与激光雷达边界响应标定的框架。该靶标作为共享观测载体：视觉基准提供带索引的图像测量值，而圆形反射率边界则提供激光雷达可观测的结构特征点。不同于将边界点直接拟合为理想几何轮廓的方法，LV-Calib能够自动裁剪背景点、估计靶标平面，并基于强度与几何约束迭代优化出精确的激光雷达侧三维特征点。该优化过程显式处理了有限光束足迹及黑白反射率跳变处混合强度回波导致的拓宽畸变过渡带。基于优化后的激光雷达特征，我们构建了融合激光雷达特征对齐的加权重投影一致性外参优化模型，其中图像观测保留在重投影域，激光雷达特征残差则依据优化置信度进行加权。最终，利用估计的外参与提取的过渡带，LV-Calib通过计算边界重叠样本的俯仰-偏航-距离残差统计量完成激光雷达边界响应标定。在印刷电路板标定数据上的实验表明，该方法实现了亚像素级重投影精度、毫米级激光雷达特征一致性，并显著提升了里程计性能。相关代码与标定数据将公开发布以支持可重复性验证。