In this work, we present a novel target-based lidar-camera extrinsic calibration methodology that can be used for non-overlapping field of view (FOV) sensors. Contrary to previous work, our methodology overcomes the non-overlapping FOV challenge using a motion capture system (MCS) instead of traditional simultaneous localization and mapping approaches. Due to the high relative precision of the MCS, our methodology can achieve both the high accuracy and repeatable calibrations of traditional target-based methods, regardless of the amount of overlap in the field of view of the sensors. We show using simulation that we can accurately recover extrinsic calibrations for a range of perturbations to the true calibration that would be expected in real circumstances. We also validate that high accuracy calibrations can be achieved on experimental data. Furthermore, We implement the described approach in an extensible way that allows any camera model, target shape, or feature extraction methodology to be used within our framework. We validate this implementation on two target shapes: an easy to construct cylinder target and a diamond target with a checkerboard. The cylinder target shape results show that our methodology can be used for degenerate target shapes where target poses cannot be fully constrained from a single observation, and distinct repeatable features need not be detected on the target.

翻译：本文提出了一种新颖的基于目标的激光雷达-相机外参标定方法，适用于非重叠视场（FOV）传感器。与以往工作不同，本方法利用运动捕捉系统（MCS）克服非重叠视场挑战，而非采用传统的同时定位与建图方法。得益于MCS的高相对精度，本方法既能实现传统基于目标方法的高精度与可重复标定，又不受传感器视场重叠程度的限制。通过仿真实验证明，该方法能在真实场景中预期的各类标定偏差下准确恢复外参标定结果。实验数据验证表明，该方法可实现高精度标定。此外，我们以可扩展方式实现所述方法，允许框架内使用任意相机模型、目标形状或特征提取方法。我们针对两种目标形状进行验证：易于构建的圆柱形目标和带有棋盘格图案的菱形目标。圆柱目标标定结果显示，本方法可处理退化目标形状——即使在单次观测中无法完全约束目标位姿，且无需检测目标上明确的重复特征。