PLK-Calib: 基于普吕弗线的单次拍摄无目标激光雷达-摄像头外参校准
PLK-Calib: Single-shot and Target-less LiDAR-Camera Extrinsic Calibration using Plücker Lines
AI总结：
本文提出基于普吕弗线的单次拍摄无目标算法，用于高精度激光雷达-摄像头外参校准，通过几何特性分解旋转和平移，提升校准精度。
摘要：
准确的激光雷达-摄像头（LC）校准对于自动驾驶系统和机器人来说具有挑战性但至关重要。在本文中，我们提出两种单次拍摄且无目标的算法，用于利用线特征估计激光雷达和摄像头之间的校准参数。第一种算法通过定义点到线投影误差来构建线到线约束，并最小化投影误差。第二种算法（PLK-Calib）利用普吕弗（PLK）坐标中线的共垂直和共平行几何特性，并将旋转和平移分解为两个约束，从而实现更精确的估计。我们的退化分析和蒙特卡洛模拟表明，三个非平行线对是估计外参的最小要求。此外，我们收集了一个在三种不同场景下具有变化外参的LC校准数据集，并使用它来评估我们提出的算法的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
