基于机器视觉的移动机器人视觉图像特征识别方法

Qin Dong
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引用次数: 0

摘要

为了提高移动机器人视觉图像特征识别的效率和准确性,本文提出了一种基于机器视觉的移动机器人视觉图像特征识别方法。首先,分析了移动机器人视觉的发展,设计了机器人视觉特征识别方法的具体功能;然后,采用傅里叶级数法采集移动机器人视觉图像,根据哈里斯算法计算与自相关函数相关的矩阵,完成移动机器人视觉图像的边缘特征提取;对移动机器人视觉图像的SIFT特征点进行分类,通过机器视觉实现移动机器人视觉图像特征识别。实验结果表明,当图像数量为 600 张时,该方法的图像特征识别准确率和图像边缘特征提取损失值分别为 96.98%和 6.38%,迭代次数为 500 次。该方法的视觉图像特征识别时间仅为 3 分钟;在不同噪声条件下,该方法的误差均值和误差方差最小。该方法能有效提高图像特征识别的效率和准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mobile Robot Vision Image Feature Recognition Method Based on Machine Vision
In order to improve the efficiency and accuracy of mobile robot visual image feature recognition, a mobile robot visual image feature recognition method based on machine vision is proposed in this paper. Firstly, the development of mobile robot vision is analyzed, and the specific functions of robot visual feature recognition method are designed; Then, the Fourier series method is used to collect the mobile robot visual image, and the matrix associated with the autocorrelation function is calculated according to the Harris algorithm to complete the edge feature extraction of the mobile robot visual image; SIFT feature points of mobile robot visual image are classified, and mobile robot visual image feature recognition is realized through machine vision. The experimental results show that when the number of images is 600, the accuracy of image feature recognition and the loss value of image edge feature extraction of this method are 96.98% and 6.38%, respectively, and the number of iterations is 500. The time of visual image feature recognition of this method is only 3 minutes; The method has the lowest error mean and error variance under different noise conditions. This method can effectively improve the efficiency and accuracy of image feature recognition.
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