| 基于改进ORB 算法的图像特征点提取与匹配方法 |
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| 作者姓名： | 杨弘凡 李 航 陈凯阳 李嘉琪 王晓菲 |
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| 作者单位： | (河南科技大学机电工程学院，河南洛阳 471003) |
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| 基金项目： | 国家重点研发计划重点专项(2018YFB200502)；河南省科技攻关项目(182102110420) |
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| 摘 要： | 针对传统ORB 算法阈值选取固定，存在误提取、误匹配，无法满足不同图像特征
点的准确提取和匹配的问题，提出了一种改进的ORB 特征点提取与匹配方法。首先设定局部
自适应阈值；然后通过像素分类，设计自适应阈值选取准则，达到ORB 特征点的精准提取；
最后在改进ORB 特征点基础上通过PROSAC 算法完成对特征点的匹配。实验结果表明，改进
后的方法对亮度变化具有较强的适应能力，计算速度和提取精度得到了提升。匹配总时间降低，
误匹配点对数量较少，正确匹配率较高，具有良好的准确性和实时性。利用匹配阶段得到的特
征点进行跟踪时得到的RMSE 误差较小，表明匹配精度得到了较大提升。和其他方法相比，具
有更好的环境适应能力和应用价值。
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| 关 键 词： | 特征点提取 局部自适应阈值 重复率 特征点对匹配 跟踪 |
Image feature points extraction and matching method based on improved ORB algorithm |
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| Authors: | YANG Hong-fan LI Hang CHEN Kai-yang LI Jia-qi WANG Xiao-fei |
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| Affiliation: | (School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang Henan 471003, China) |
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| Abstract: | The fixed threshold selection of traditional ORB algorithm results in many false extractions
and mismatches, which cannot meet the requirements of accurate extraction and matching of different
image feature points. To solve this problem, an improved ORB feature point extraction and matching
method was proposed. Firstly, the local adaptive threshold was set up. Then, an adaptive threshold
selection criterion was designed by classifying the pixels, and thus the precise extraction of ORB
feature points was achieved. Finally, the PROSAC algorithm was used to complete the matching of
feature points based on the improved ORB feature points. The experimental results indicate that the
improved method has a high adaptability to variations in brightness, and both the calculation speed
and extraction accuracy are greatly improved. The total matching time is reduced, the number of
mismatches is less, and the accurate matching rate is increased, which indicates that this improved
method is characterized with accuracy and real-time performance. In addition, the RMSE error
obtained by tracking the feature points acquired at the matching stage is small, which demonstrates a
significant improvement in matching accuracy. Compared with other existing methods, this method
has better environmental adaptive capacity and application value. |
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| Keywords: | feature point extraction local adaptive threshold repetition rate point pairs matching tracking |
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