Multi-camera calibration with pattern rigs, including for non-overlapping cameras: CALICO
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2019
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| _version_ | 1866911817137127424 |
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| author | Tabb, Amy Medeiros, Henry Feldmann, Mitchell J. Santos, Thiago T. |
| author_facet | Tabb, Amy Medeiros, Henry Feldmann, Mitchell J. Santos, Thiago T. |
| contents | 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}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1903_06811 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | Multi-camera calibration with pattern rigs, including for non-overlapping cameras: CALICO Tabb, Amy Medeiros, Henry Feldmann, Mitchell J. Santos, Thiago T. Computer Vision and Pattern Recognition 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}. |
| title | Multi-camera calibration with pattern rigs, including for non-overlapping cameras: CALICO |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/1903.06811 |