用直线特征实现无需标靶的单次激光雷达-相机校准
PLK-Calib: Single-shot and Target-less LiDAR-Camera Extrinsic Calibration using Plücker Lines
- 基于普吕克坐标直线几何特性，解耦旋转与平移约束
- 仅需三组非平行直线对即可完成校准，精度更高
- 适用于自动驾驶和机器人，无需特殊标定物
精确的激光雷达-相机（LC）标定对自动驾驶和机器人系统至关重要但极具挑战。本文提出两种单次、无标靶的算法，利用线特征估计激光雷达与相机之间的外参。第一种算法通过定义点到线投影误差构建线对线约束，并最小化该误差。第二种算法（PLK-Calib）利用普吕克（PLK）坐标系中直线的共垂与共线几何特性，将旋转与平移解耦为两个独立约束，从而获得更优估计。退化分析与蒙特卡洛仿真表明，三个非平行线段对是估计外参的最小需求。此外，我们收集了一个包含三种不同外参场景的LC标定数据集，并用于评估所提算法性能。
原文摘要 · Abstract (English)
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.
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