Robust Plane-Based Calibration of Multiple Non-Overlapping Cameras

Chen Zhu, Zihan Zhou, Ziran Xing, Yanbing Dong, Yi Ma, Jingyi Yu
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引用次数: 13

Abstract

The availability of commodity multi-camera systems such as Google Jump, Jaunt, and Lytro Immerge have brought new demand for reliable and efficient extrinsic camera calibration. State-of-the-art solutions generally require that adjacent, if not all, cameras observe a common area or employ known scene structures. In this paper, we present a novel multi-camera calibration technique that eliminates such requirements. Our approach extends the single-pair hand-eye calibration used in robotics to multi-camera systems. Specifically, we make use of (possibly unknown) planar structures in the scene and combine plane-based structure from motion, camera pose estimation, and task-specific bundle adjustment for extrinsic calibration. Experiments on several multi-camera setups demonstrate that our scheme is highly accurate, robust, and efficient.
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基于鲁棒平面的多相机非重叠标定
谷歌Jump、Jaunt和Lytro Immerge等商用多相机系统的出现,为可靠、高效的外部相机校准带来了新的需求。最先进的解决方案通常要求相邻的(如果不是全部的话)摄像机观察公共区域或使用已知的场景结构。在本文中,我们提出了一种新的多相机校准技术,消除了这些要求。我们的方法将机器人中使用的单对手眼校准扩展到多相机系统。具体来说,我们在场景中使用(可能未知的)平面结构,并结合来自运动、相机姿态估计和任务特定束调整的平面结构进行外部校准。实验结果表明,该方法具有较高的精度、鲁棒性和有效性。
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