Multi-camera calibration with pattern rigs, including for non-overlapping cameras: CALICO
This addresses calibration for multi-camera systems in robotics or computer vision, offering a practical solution for scenarios where existing methods are constrained, though it appears incremental as it builds on pattern-based approaches.
The paper tackles the problem of multi-camera calibration in challenging contexts like non-overlapping or non-synchronized cameras by introducing CALICO, a pattern-based method using pattern rigs, which achieved mean reconstruction accuracy errors of ≤0.71 mm for real rigs and ≤1.11 mm for simulated rigs.
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}.