Abstract
Many objects in the real world have circular feature. In general, circular feature’s pose is represented by 5-DoF (degree of freedom) vector ξ = X , Y , Z , α , β T . It is a difficult task to measure the accuracy of circular feature’s pose in each direction and the correlation between each direction. This paper proposes a closed-form solution for estimating the accuracy of pose transformation of circular feature. The covariance matrix of ξ is used to measure the accuracy of the pose. The relationship between the pose of the circular feature of 3D object and the 2D points is analyzed to yield an implicit function, and then Gauss–Newton theorem is employed to compute the partial derivatives of the function with respect to such point, and after that the covariance matrix is computed from both the 2D points and the extraction error. In addition, the method utilizes the covariance matrix of 5-DoF circular feature’s pose variables to optimize the pose estimator. Based on pose covariance, minimize the mean square error (Min-MSE) metric is introduced to guide good 2D imaging point selection, and the total amount of noise introduced into the pose estimator can be reduced. This work provides an accuracy method for object 2D-3D pose estimation using circular feature. At last, the effectiveness of the method for estimating the accuracy is validated based on both random data sets and synthetic images. Various synthetic image sequences are illustrated to show the performance and advantages of the proposed pose optimization method for estimating circular feature’s pose.
Highlights
Pose estimation is an essential step in many machine vision and photogrammetric applications, and the ultimate goal of pose estimation is to identify 3D pose of an object of interest from an image or image sequence [1, 2]. e existing algorithms detect elliptic from 2D image, and the 3D pose of the circular can be extracted from single image using the inverse projection model of the calibrated camera (see Figure 1(b)) [3,4,5]. ese methods are successfully applied to pose estimation of underwater dock [6] and pose estimation of the bait [7]
For the application of the monocular vision pose estimation system using circular feature in industrial, quite a few works on accuracy are presented; industrial application needs very high requirement to precision. e process of pose estimation is so complicated that it is difficult to observe the effect of the each parameter error on pose error and measure the accuracy of the circular feature’s pose
We propose a closedform solution for estimating the covariance matrix of 5D circular feature’s pose variables by exploiting 2D imaging point’s coordinates and its extraction error. e main contributions of the present work can be summarized as follows: (1) e paper proposes a closed-form solution for estimating the accuracy of 5D circular feature’s pose variables using 2D imaging point’s coordinate and its extraction error covariance matrix
Summary
Pose estimation is an essential step in many machine vision and photogrammetric applications, and the ultimate goal of pose estimation is to identify 3D pose of an object of interest from an image or image sequence [1, 2]. e existing algorithms detect elliptic from 2D image, and the 3D pose of the circular can be extracted from single image using the inverse projection model of the calibrated camera (see Figure 1(b)) [3,4,5]. ese methods are successfully applied to pose estimation of underwater dock [6] and pose estimation of the bait [7]. We propose a closedform solution for estimating the covariance matrix of 5D circular feature’s pose variables by exploiting 2D imaging point’s coordinates and its extraction error. (1) e paper proposes a closed-form solution for estimating the accuracy of 5D circular feature’s pose variables using 2D imaging point’s coordinate and its extraction error covariance matrix. E proposed algorithm provides a closed-form solution for estimating the covariance matrix of circular feature’s pose variables instead of Monte Carlo simulation.
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