Multi-camera calibration with pattern rigs, including for non-overlapping cameras: CALICO

TL;DR

CALICO is a pattern-based multi-camera calibration method that effectively handles challenging scenarios like non-overlapping, stationary, and non-synchronized cameras, outperforming existing approaches in accuracy.

Contribution

It introduces a novel pattern rig-based calibration approach formulated through algebraic and reprojection error minimization, suitable for complex multi-camera setups.

Findings

Achieved mean reconstruction error ≤ 0.71 mm in real rigs.

Outperformed Kalibr in various experiments.

Effective for non-overlapping and non-synchronized cameras.

Abstract

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}.