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，可用于非重叠视场传感器。与先前工作不同，本方法采用运动捕捉系统而非传统同步定位与建图方法来解决非重叠视场挑战。得益于MCS的高相对精度，无论传感器视场重叠程度如何，本方法均可实现传统基于目标方法的高精度与可重复标定。通过仿真实验证明，针对真实场景中可能出现的各类标定参数扰动，本方法能够准确恢复外参标定结果。同时通过实验数据验证了高精度标定的可行性。此外，本方法以可扩展方式实现，框架内可兼容任意相机模型、目标形状或特征提取方法。我们在两种目标形状上验证了该实现：易于构建的圆柱形目标与带有棋盘格图案的菱形目标。圆柱形目标结果表明，本方法可处理退化目标形状（单次观测无法完全约束目标位姿），且无需在目标上检测明显的可重复特征。