Calibration of Asynchronous Camera Networks: CALICO

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.