  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}.
