| 行芳仪,徐 成,高宏伟.高效高精度光照自适应的 ORB 特征匹配算法[J].电子测量与仪器学报,2023,37(7):140-147 |
| 高效高精度光照自适应的 ORB 特征匹配算法 |
| Efficient and high-precision illumination adaptiveORB feature matching algorithm |
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| DOI： |
| 中文关键词: ORB 算法 特征检测 特征匹配 四叉树分解法 RANSAC 算法 |
| 英文关键词:ORB algorithm feature detection feature matching quadtree decomposition method RANSAC algorithm |
| 基金项目:辽宁省重点科技创新基地联合开放基金(2021-KF-12-05)、国家重点研发计划(2018YFB1304600)项目资助 |
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| Author | Institution |
| Xing Fangyi | 1. School of Automation & Electrical Engineering, Shenyang Ligong University，2. Shenyang Institute of Automation, Chinese Academy of Sciences, State Key Laboratory of Robotics |
| Xu Cheng | 2. Shenyang Institute of Automation, Chinese Academy of Sciences, State Key Laboratory of Robotics,3. School of Instrument Science and Engineering, Southeast University |
| Gao Hongwei | 1. School of Automation & Electrical Engineering, Shenyang Ligong University，2. Shenyang Institute of Automation, Chinese Academy of Sciences, State Key Laboratory of Robotics |
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| 中文摘要: |
| 针对非均匀光照下 ORB 图像特征检测算法存在特征点过于聚集、匹配准确率不高等问题,提出了一种高效高精度光
照自适应的 ORB 图像特征匹配算法。 利用自适应阈值提取待测图像的 oFAST 特征点,通过优化的四叉树分解法均匀分配,进
一步提高了低照度或高曝光区域特征点的数量,随后,根据汉明距离进行特征匹配,使用改进的 RANSAC 算法剔除误匹配,提
高 ORB 算法中特征点的匹配准确率。 实验结果表明,针对具有明显光照变化的数据集,相较于 ORB、MA、Y-ORB 及 S-ORB 算
法,本文算法的平均特征分布均匀度提高 13. 1%,特征提取时间节省 26. 3%,综合评价指标提升 18. 5%,可高效完成复杂场景变
化下的特征匹配,对目标识别和三维重建等领域具有较强的应用价值。 |
| 英文摘要: |
| In order to solve the problems of the ORB image feature detection algorithm under non-uniform illumination, such as overly
clustered feature points and low accuracy of feature matching, we propose an efficient and high-precision illumination adaptive ORB
image feature matching algorithm. The oFAST feature points of the image to be measured are extracted using the adaptive threshold, and
the number of feature points in the low illumination or high exposure area is further increased through the uniform distribution of the
optimized quadtree decomposition method. Then, feature matching is performed according to Hamming distance, and the improved
RANSAC algorithm is used to eliminate mis-matching, so as to improve the matching accuracy of the feature points in the ORB
algorithm. The experimental results show that for data sets with obvious illumination changes, compared with ORB, MA, Y-ORB and SORB algorithms, the average feature distribution uniformity of our proposed algorithm is improved by 13. 1%, the feature extraction time
is saved by 26. 3%, and the comprehensive evaluation index is improved by 18. 5%. It can efficiently complete feature matching under
complex environment changes, and has strong application value in the fields of target recognition and 3D reconstruction. |
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