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Monocular position and pose measurement method based on surface topography of object
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摘要
为了获取在风洞实验中运动物体的位姿变化，提出了一种融合物体表面三维形貌信息的单相机视觉位姿测量方法。该方法以多点透视成像原理作为求解物体位姿变化的基础，以物体的图像特征角点作为特征点，并利用物体表面三维形貌模型信息获得特征点的三维坐标。通过实验完成了该测量方法的精度验证，在400 mm的观察距离上，位移平均测量误差为0.03 mm，均方根误差为0.234 mm；俯仰角、偏航角与滚转角的平均误差分别为0.08°、0.1°与0.09°，均方根误差分别为0.485°、0.312°与0.442°。实验结果表明该方法有可用于实用的测量精度。
Abstract
In order to obtain the change of posture of moving objects in wind tunnel experiment, a method of single-camera visual pose measurement based on three-dimensional topography model of object surface is proposed. The method uses the multi-perspective imaging principle to solve the target pose, obtains the feature corner point on the target as the characteristic point needed for the solution, and proposes to use target's 3D surface topography information to obtain the geometric relationships between feature points. In this paper, the accuracy of the measurement method is verified under the laboratory conditions. The average accuracy of displacement is 0.23 mm and the mean square error is 0.234 mm. The accuracy of the pitch angle, yaw angle and roll angle are 0.08°, 0.1°and 0.09°, respectively, and the mean square error are 0.485°, 0.312°and 0.442°. The experimental results show that the method can be used for practical measurement.
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Key words:
- machine vision /
- pose measurement /
- monocular vision /
- feature match /
- surface topography /
- 3D reconstruction
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Overview
Overview: In order to obtain the change of posture of moving objects in wind tunnel experiment, this paper presents a single camera pose and position measurement method which integrates the three-dimensional topography of object surface. The traditional monocular visual pose measurement method has to install optical mark points on the object. The 3D coordinate of the mark point has been determined at the time of installation. Then get the image coordinate of the optical mark point from pictures to calculate the pose change of the object. The disadvantages of the traditional calculation method are the complicated steps, the number of mark points is too few and they can easily be blocked, and they will distort the surface structure of the object. The surface of the measured object cannot install optical mark point, so the method needs to use the object's own image properties to set feature points.
The measurement method proposed takes multi-point perspective imaging theory as the basis for solving the pose change of objects, takes the image feature corner of the object as the feature point, and then obtains the three-dimensional coordinates of the feature points by using the three-dimensional topography model of the object surface. The three-dimensional topography model of an object is obtained using the SFM multi-view 3D reconstruction method. Finally, the RPnP algorithm is used to calculate the image coordinates and the three-dimensional coordinates of the feature points to obtain the pose change of the object.
The basic principle of pose solution is introduced. The process of SFM reconstruction, feature point matching and filtering process based on grid motion estimation are introduced briefly. The method of using 3D surface topography model to calculate the image feature corner's 3D coordinates is described in detail. And analyze the characteristics of three-dimensional coordinates of the extraction accuracy.
The experiment is carried out under laboratory conditions to verify the accuracy of the measurement method. At the observation distance of 400 mm, the error of the average displacement measurement is 0.03 mm and the root mean square error is 0.234 mm. The average error of pitch angle, yaw angle and roll angle are 0.08°, 0.1° and 0.09°, RMSE are 0.485°, 0.312° and 0.442°. Experimental results show that the method can be used for practical measurement accuracy.
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图 1 运动中的物体与相机的位姿关系
Figure 1. The relationship between the target and the camera before and after movement
图 5 匹配结果对比。(a) GMS匹配结果；(b) RANSAC匹配结果
Figure 5. Comparison of matching results. (a) GMS matching results; (b) RANSAC matching results
图 8 姿态角测量误差结果。(a)偏航角测量误差；(b)滚转角测量误差；(c)俯仰角测量误差
Figure 8. Angle measurement error results. (a) Yaw angle measurement error; (b) Roll angle measurement error; (c) Pitch angle measurement error
表 1 特征点的重建误差表
Table 1. Reconstruction error of feature points
x/mm y/mm z/mm d/mm mean -0.001 0.002 0.001 0.154 RMSE 0.081 0.110 0.101 0.169 
下载: 导出CSV
表 2 相机内参标定结果
Table 2. Calibration result of the camera
fx/pixels fy/pixels u0/pixels v0/pixels kc1 kc2 3439.7 3439.4 676.9 562.9 -0.086 0.442
下载: 导出CSV
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