  Camera network and multi-camera calibration for external parameters is a
necessary step for a variety of contexts in computer vision and robotics,
ranging from three-dimensional reconstruction to human activity tracking. This
paper describes CALICO, a method for camera network and/or multi-camera
calibration suitable for challenging contexts: the cameras may not share a
common field of view and the network may be asynchronous. The calibration
object required is one or more rigidly attached planar calibration patterns,
which are distinguishable from one another, such as aruco or charuco patterns.
  We formulate the camera network and/or multi-camera calibration problem using
rigidity constraints, represented as a system of equations, and an approximate
solution is found through a two-step process. Simulated and real experiments,
including an asynchronous camera network, multicamera system, and rotating
imaging system, demonstrate the method in a variety of settings. Median
reconstruction accuracy error was less than $0.41$ mm$^2$ for all datasets.
This method is suitable for novice users to calibrate a camera network, and the
modularity of the calibration object also allows for disassembly, shipping, and
the use of this method in a variety of large and small spaces.

Computer Vision and Pattern Recognition