基于分层空间一致性的旋转双目立体校正算法

罗其俊; 田鑫; 高庆吉

doi:10.13700/j.bh.1001-5965.2022.0611

基于分层空间一致性的旋转双目立体校正算法

doi: 10.13700/j.bh.1001-5965.2022.0611

罗其俊^1, ,,
田鑫^{1, 2,},
高庆吉¹

1.
中国民航大学机器人研究所，天津 300300
2.
北京航空航天大学机械工程及自动化学院，北京 100191

基金项目: 天津市教委科研计划(2019KJ118)

详细信息

通讯作者:
E-mail：qjluo@cauc.edu.cn

中图分类号: TP391.41；TP18
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- 被引次数: 0
出版历程
- 收稿日期: 2022-07-12
- 录用日期: 2022-08-14
- 网络出版日期: 2022-12-14
- 整期出版日期: 2024-05-29

Rotation binocular stereo rectification algorithm based on hierarchical spatial consistency

LUO Qijun^{1
, ,},
TIAN Xin^{1, 2
,},
GAO Qingji¹

1.
Robotices Institute，Civil Aviation University of China，Tianjin 300300，China
2.
School of Mechanical Engineering and Automation，Beihang University，Beijing 100191，China

Funds: Scientific Research Plan of Tianjin Education Commission (2019KJ118)

More Information

Corresponding author: E-mail：qjluo@cauc.edu.cn

摘要

摘要:
在旋转双目立体视觉系统中，转台机械间隙导致的左右相机旋转平移偏差，造成立体校正图像的严重畸变。针对该问题，提出一种基于分层空间一致性的旋转双目立体校正算法。采用ORB特征在原始左右图像中进行快速全局立体匹配，设计一种新的特征点全局双层约束，实现匹配点的优选。提出基于内点邻域空间一致性的局部校验方法，实现二次匹配优化，并利用质量排序的优化匹配点集，由八点法基础矩阵估计算法计算左右相机的精确位姿关系，以此完成图像的立体校正。在Oxford和SYNTIM数据集上的典型算法对比实验，验证了所提算法的性能。多角度立体校正实验表明：所提算法可适应光轴夹角变化，在双目最大45°夹角时保证立体校正的质量，匹配点偏差小于0.2像素。
- 立体校正 /
- 旋转相机 /
- 基础矩阵 /
- 空间一致性 /
- 位姿估计
Abstract:
The mechanical gap of the turntable in the rotating binocular stereo vision system causes the left and right cameras to rotate and deviate in translation, severely distorting the stereo rectification image. To solve this problem, a rotating binocular stereo rectification algorithm based on hierarchical spatial consensus is proposed. Firstly, oriented FAST and rotated BRIEF (ORB) features are used for the fast global stereo matching in the original images, and a new global double-layer constraint of feature points is defined to realize the preferred selection of the matching points. Then, a local verification method based on the consensus of the neighborhood space of the inliers is proposed to realize the secondary matching optimizations and the matching points are optimized by quality sorting. To finish the stereo rectification of the pictures, the eight-point method-based fundamental matrix estimation algorithm determines the precise pose relationship between the left and right cameras. The comparison experiments of typical algorithms on Oxford and SYNTIM datasets verify the proposed algorithm's performance. The multi-angle stereo rectification experiment shows that the proposed algorithm can adapt to the change of optical axis angle, and ensure the quality of stereo rectification when the maximum angle of binocular is 45°. The deviation of the matching point is less than 0.2 pixels.
- stereo rectification /
- rotating camera /
- fundamental matrix /
- spatial consensus /
- pose estimation

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图 1 旋转双目立体视觉模型

Figure 1. Rotating binocular stereo vision model

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图 2 本文算法流程

Figure 2. Flow of the proposed algorithm

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图 3 FAST特征检测

Figure 3. FAST feature detection

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图 4 图像立体校正过程

Figure 4. Image stereo rectification process

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图 5 匹配点分层优选和优化

Figure 5. Matching points hierarchical preference and optimisation

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图 6 图像立体校正过程

Figure 6. Image stereo rectification process

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图 7 本文算法的多角度校正结果

Figure 7. Rectification result of proposed algorithm in multi-angles

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图 8 5°时的校正结果对比

Figure 8. Comparison of rectification results at 5°

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图 9 30°时的校正结果对比

Figure 9. Comparison of rectification results at 30°

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图 10 40°时的校正结果对比

Figure 10. Comparison of rectification results at 40°

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图 11 不同角度下校正误差对比

Figure 11. Rectification error comparison at different angles

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表 1 匹配点分层优选和优化

Table 1. Hierarchical selection and optimization of matching points

图像	初始匹配点数	匹配点优选点数	二次匹配优化点数
Graf	400	175	125

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表 2 立体校正精度统计

Table 2. Stereo rectification accuracy statistics 像素

校正图像	E_H				E_M				E_V
校正图像	BF	LM-RANSAC	GC-RANSAC	本文	BF	LM-RANSAC	GC-RANSAC	本文	BF	LM-RANSAC	GC-RANSAC	本文
Building	7.4649	3.4254	3.1222	0.72	1.4629	0.4515	0.50001	0.1546	2.1609	1.2011	1.22001	0.4501
Baballe	24.4806	5.3412	5.9917	1.20001	3.2711	0.7122	0.6215	0.2056	4.4922	1.5522	1.3271	0.5132
Graf	4.728	2.5999	3.3781	0.7991	1.5077	0.4512	0.3189	0.1921	2.32	0.6801	0.6674	0.2312
Sport	212.004	12.1162	14.5999	3.33	89.3941	3.2433	2.6485	0.4975	86.458	4.1167	3.7247	1.2898
NBuste	234.3337	25.1242	22.5441	18.5327	111.9321	4.2311	2.0577	2.2262	139.2001	5.6822	3.8501	4.1999

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