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Using Layered Color Precision for a Self-Calibrating Vision System

机译:使用分层颜色精度进行自校准视觉系统

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This paper presents a vision system for robotic soccer which was tested on Sony's four legged robot Aibo. The input for the vision system are images of the camera and the sensor readings of the robot's head joints, the output are the positions of all recognized objects in relation to the robot. The object recognition is based on the colors of the objects and uses a color look-up table. The vision system creates the color look-up table on its own during a soccer game. Thus no pre-run calibration is needed and the robot can cope with inhomogeneous or changing light on the soccer field. It is shown, how different layers of color representation can be used to refine the results of color classification. However, the self-calibrated color look-up table is not as accurate as a hand-made. Together with the introduced object recognition which is very robust relating to the quality of the color table, the self-calibrating vision works very well. This robustness is achieved using the detection of edges on scan lines.
机译:本文为机器人足球提供了一个愿景系统,该系统在索尼的四条腿机器人Aibo上进行了测试。视觉系统的输入是摄像机的图像和机器人头部接头的传感器读数,输出是与机器人相关的所有识别对象的位置。对象识别基于对象的颜色,并使用颜色查找表。 Vision系统在足球比赛期间创建了颜色查找表。因此,不需要预先运行校准,机器人可以在足球场上应对非均匀或改变的光。显示了,如何使用不同的颜色表示层来改进颜色分类的结果。但是,自我校准的颜色查找表与手工制作的颜色查找表并不准确。与介绍的对象识别一起是非常坚固的与颜色表的质量有关,自校准视觉效果很好。使用在扫描线上的边缘检测来实现这种稳健性。

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