A self-calibration technique for active vision systems
A self-calibration technique for active vision systems
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DOI：
10.1109/70.481755
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发表时间：
1996-02-01
期刊：
影响因子：
--
通讯作者：
DeMa, S
中科院分区：
文献类型：
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作者：
DeMa, S
许多视觉研究小组开发了主动视觉平台，可以控制相机的运动。类似的设置是用于机器人操纵器的腕式摄像头。这种头眼（或手眼）设置极大地促进了运动立体、对象跟踪和主动感知。使用主动视觉系统的重要问题之一是确定相机相对于相机平台的位置和方向。这个问题在主动视觉中称为头眼校准，在机器人技术中称为手眼校准。在本文中，我们提出了一种校准头眼（或手眼）几何形状以及相机内在参数的新技术。该技术允许相机自校准，因为它不需要参考物体并直接使用环境图像。相机自校准非常重要，尤其是在执行底层视觉任务不允许使用参考对象的情况下。我们的方法利用了主动视觉系统的灵活性，并将相机校准基于一系列专门设计的运动。结果表明，如果先验地已知相机内在参数，则可以使用 3 个纯平移运动来求解相机相对于平台的方向。如果内在参数未知，则需要两个运动序列（每个序列由三个正交平移组成）来确定相机方向和内在参数。一旦确定了相机方向和固有参数，就可以根据平台的任意非平移运动来计算相机相对于平台的位置。我们方法中的所有计算都是线性的。本文给出了真实图像的实验结果。
Many vision research groups have developed the active vision platform whereby the camera motion can be controlled. A similar setup is the wrist-mounted camera for a robot manipulator. This head-eye (or hand-eye) setup considerably facilitates motion stereo, object tracking, and active perception. One of the important issues in using the active vision system is to determine the camera position and orientation relative to the camera platform. This problem is called the head-eye calibration in active vision, and the hand-eye calibration in robotics.In this paper we present a new technique for calibrating the head-eye (or hand-eye) geometry as well as the camera intrinsic parameters. The technique allows camera self-calibration because it requires no reference object and directly uses the images of the environment. Camera self-calibration is important especially in circumstances where the execution of the underlying visual tasks does not permit the use of reference objects. Our method exploits the flexibility of the active vision system, and bases camera calibration on a sequence of specially designed motion. It is shown that if the camera intrinsic parameters are known a priori, the orientation of the camera relative to the platform can be solved using 3 pure translational motions. If the intrinsic parameters are unknown, then two sequences of motion, each consisting of three orthogonal translations, are necessary to determine the camera orientation and intrinsic parameters. Once the camera orientation and intrinsic parameters are determined, the position of the camera relative to the platform can be computed from an arbitrary nontranslational motion of the platform. All the computations in our method are linear. Experimental results with real images are presented in this paper.