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基于近景摄影测量的非重叠相机网络外部校准

Extrinsic calibration of a non-overlapping camera network based on close-range photogrammetry.

作者信息

Dong Shuai, Shao Xinxing, Kang Xin, Yang Fujun, He Xiaoyuan

出版信息

Appl Opt. 2016 Aug 10;55(23):6363-70. doi: 10.1364/AO.55.006363.

Abstract

In this paper, an extrinsic calibration method for a non-overlapping camera network is presented based on close-range photogrammetry. The method does not require calibration targets or the cameras to be moved. The visual sensors are relatively motionless and do not see the same area at the same time. The proposed method combines the multiple cameras using some arbitrarily distributed encoded targets. The calibration procedure consists of three steps: reconstructing the three-dimensional (3D) coordinates of the encoded targets using a hand-held digital camera, performing the intrinsic calibration of the camera network, and calibrating the extrinsic parameters of each camera with only one image. A series of experiments, including 3D reconstruction, rotation, and translation, are employed to validate the proposed approach. The results show that the relative error for the 3D reconstruction is smaller than 0.003%, the relative errors of both rotation and translation are less than 0.066%, and the re-projection error is only 0.09 pixels.

摘要

本文提出了一种基于近景摄影测量的非重叠相机网络外部校准方法。该方法不需要校准靶标,也不需要移动相机。视觉传感器相对静止,且不同时观察同一区域。所提出的方法使用一些任意分布的编码靶标来组合多个相机。校准过程包括三个步骤:使用手持数码相机重建编码靶标的三维(3D)坐标,对相机网络进行内参数校准,以及仅用一幅图像校准每个相机的外参数。采用了一系列实验,包括三维重建、旋转和平移实验,来验证所提出的方法。结果表明,三维重建的相对误差小于0.003%,旋转和平移的相对误差均小于0.066%,重投影误差仅为0.09像素。

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