使用图案装置的多相机标定，包括非重叠视场相机：CALICO
Multi-camera calibration with pattern rigs, including for non-overlapping cameras: CALICO
AI总结：
CALICO利用图案装置中图案与相机间的刚性约束，将多相机标定转化为代数与重投影误差最小化问题，适用于非重叠视场、非同步及静止或移动多相机系统，实验表明其精度优于Kalibr。
摘要：
本文介绍了CALICO，一种适用于挑战性场景的多相机标定方法：静止和移动多相机系统、无重叠视场的相机以及非同步相机。近年来的方法大致分为基于基础设施和基于图案两类。基于基础设施的方法利用场景特征进行标定，而基于图案的方法使用标定图案。基于基础设施的方法不适用于静止相机系统，基于图案的方法可能限制相机摆放位置，因为需要共享视场或极大的图案。CALICO是一种基于图案的方法，其中多相机标定问题通过图案与相机之间的刚性约束来表述。我们使用一种图案装置：多个图案彼此刚性连接或固定于某个结构上。我们将标定问题表述为代数误差和重投影误差最小化问题。仿真和真实实验在多种设置下验证了该方法。CALICO与Kalibr相比表现更优。真实相机装置的平均重建精度误差≤0.71 mm，仿真相机装置≤1.11 mm。代码和数据发布于\cite{tabb_amy_2019_3520866}和\url{https://github.com/amy-tabb/calico}。
英文摘要：
This paper describes CALICO, a method for multi-camera calibration suitable for challenging contexts: stationary and mobile multi-camera systems, cameras without overlapping fields of view, and non-synchronized cameras. Recent approaches are roughly divided into infrastructure- and pattern-based. Infrastructure-based approaches use the scene's features to calibrate, while pattern-based approaches use calibration patterns. Infrastructure-based approaches are not suitable for stationary camera systems, and pattern-based approaches may constrain camera placement because shared fields of view or extremely large patterns are required. CALICO is a pattern-based approach, where the multi-calibration problem is formulated using rigidity constraints between patterns and cameras. We use a {\it pattern rig}: several patterns rigidly attached to each other or some structure. We express the calibration problem as that of algebraic and reprojection error minimization problems. Simulated and real experiments demonstrate the method in a variety of settings. CALICO compared favorably to Kalibr. Mean reconstruction accuracy error was $\le 0.71$ mm for real camera rigs, and $\le 1.11$ for simulated camera rigs. Code and data releases are available at \cite{tabb_amy_2019_3520866} and \url{https://github.com/amy-tabb/calico}.
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。
