Accurate LiDAR-Camera (LC) calibration is challenging but crucial for autonomous systems and robotics. In this paper, we propose two single-shot and target-less algorithms to estimate the calibration parameters between LiDAR and camera using line features. The first algorithm constructs line-to-line constraints by defining points-to-line projection errors and minimizes the projection error. The second algorithm (PLK-Calib) utilizes the co-perpendicular and co-parallel geometric properties of lines in Plücker (PLK) coordinate, and decouples the rotation and translation into two constraints, enabling more accurate estimates. Our degenerate analysis and Monte Carlo simulation indicate that three nonparallel line pairs are the minimal requirements to estimate the extrinsic parameters. Furthermore, we collect an LC calibration dataset with varying extrinsic under three different scenarios and use it to evaluate the performance of our proposed algorithms.

翻译：精确的激光雷达-相机（LC）标定对于自主系统和机器人技术至关重要，但极具挑战性。本文提出了两种单次、无目标的算法，利用线特征来估计激光雷达与相机之间的标定参数。第一种算法通过定义点到线的投影误差来构建线到线约束，并最小化该投影误差。第二种算法（PLK-Calib）利用普吕克（PLK）坐标中线特征的共垂直与共平行几何特性，将旋转和平移解耦为两个独立的约束，从而能够实现更精确的估计。我们的退化性分析和蒙特卡洛模拟表明，三对非平行线对是估计外参所需的最小条件。此外，我们在三种不同场景下采集了一个具有变化外参的LC标定数据集，并利用该数据集评估了我们所提出算法的性能。