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Lidar-Camera外参标定方法,可用于非重叠视场(FOV)的传感器。与先前的工作相反,我们的方法使用运动捕捉系统(MCS)来解决非重叠FOV的挑战,而不是传统的同时定位和映射方法。由于MCS的相对精度较高,我们的方法可以实现传统基于目标方法的高精度和可重复标定,而与传感器视场的重叠量无关。我们使用仿真显示,我们可以准确地恢复各种扰动下的外参标定,这些扰动在实际情况下是可以预期的。我们还验证了可以在实验数据上实现高精度的标定。此外,我们以可扩展的方式实现了所描述的方法,允许在我们的框架内使用任何相机模型、目标形状或特征提取方法。我们验证了此实现的两个目标形状:易于构造的圆柱形目标和带有棋盘格的钻石形目标。圆柱形目标形状的结果表明,我们的方法可用于退化的目标形状,在这些形状中,单个观察无法完全约束目标姿势,并且不需要在目标上检测出明显可重复的特征。