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Using Layered Color Precision for a Self-Calibrating Vision System

机译:将分层色彩精度用于自校准视觉系统

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摘要

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:本文介绍了一种用于机器人足球的视觉系统,该系统已在索尼的四足机器人Aibo上进行了测试。视觉系统的输入是摄像机的图像以及机器人头部关节的传感器读数,输出是所有已识别物体相对于机器人的位置。对象识别基于对象的颜色,并使用颜色查找表。视觉系统在足球比赛期间自行创建颜色查询表。因此,无需进行运行前校准,并且机器人可以应对足球场上不均匀或变化的光线。显示了如何使用不同的颜色表示层来细化颜色分类的结果。但是,自校准颜色查询表的准确性不如手工制作。加上引入的与色表质量相关的非常强大的目标识别,自校准视觉效果很好。通过检测扫描线上的边缘可以实现这种鲁棒性。

著录项

  • 来源
    《RoboCup Symposium; 2004; Lisbon(PT)》|2004年|P.209-220|共12页
  • 会议地点 Lisbon(PT)
  • 作者

    Matthias Juengel;

  • 作者单位

    Institut fuer Informatik, LFG Kuenstliche Intelligenz, Humboldt-Universitaet zu Berlin, Unter den Linden 6, 10099 Berlin, Germany;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 机器人技术;
  • 关键词

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