A Novel Method for Intrinsic and Extrinsic Parameters Estimation by Solving Perspective-Three-Point Problem with Known Camera Position

Guo, Kai; Hu, Ye; Gu, Junhao; Chen, Honglin

doi:10.3390/app11136014

Cited by 15 publications

(14 citation statements)

References 30 publications

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“…The camera position is generally given by RTK or the total station, whose accuracy is better than 3 cm or 0.5 cm, respectively. Hence, we added zero-mean Gaussian noise to the camera position, varying the noise deviation level from 0 to 3 cm [ 50 , 51 ]. In addition, the outlier ratio was set to 30% as suggested in [ 1 ].…”

Section: Experiments and Resultsmentioning

confidence: 99%

An Accurate and Robust Method for Absolute Pose Estimation with UAV Using RANSAC

Guo

Gao

et al. 2022

Sensors

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In this paper, we proposed an accurate and robust method for absolute pose estimation with UAV (unmanned aerial vehicle) using RANSAC (random sample consensus). Because the artificial 3D control points with high accuracy are time-consuming and the small point set may lead low measuring accuracy, we designed a customized UAV to efficiently obtain mass 3D points. A light source was mounted on the UAV and used as a 3D point. The position of the 3D point was given by RTK (real-time kinematic) mounted on the UAV, and the position of the corresponding 2D point was given by feature extraction. The 2D–3D point correspondences exhibited some outliers because of the failure of feature extraction, the error of RTK, and wrong matches. Hence, RANSAC was used to remove the outliers and obtain the coarse pose. Then, we proposed a method to refine the coarse pose, whose procedure was formulated as the optimization of a cost function about the reprojection error based on the error transferring model and gradient descent to refine it. Before that, normalization was given for all the valid 2D–3D point correspondences to improve the estimation accuracy. In addition, we manufactured a prototype of a UAV with RTK and light source to obtain mass 2D–3D point correspondences for real images. Lastly, we provided a thorough test using synthetic data and real images, compared with several state-of-the-art perspective-n-point solvers. Experimental results showed that, even with a high outlier ratio, our proposed method had better performance in terms of numerical stability, noise sensitivity, and computational speed.

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Section: Experiments and Resultsmentioning

confidence: 99%

An Accurate and Robust Method for Absolute Pose Estimation with UAV Using RANSAC

Guo

Gao

et al. 2022

Sensors

Self Cite

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“…In our problem, we assume that the skew is zero, the aspect ratio of the pixels is one, and the principal point is the center of the image, which is true for most modern digital cameras and can yield good results, even when they are not exactly satisfied; as will be shown in the experiments [3,33]. In this paper, the camera position O c (X Oc , Y Oc , Z Oc ) is known, which can be obtained by positioning sensors [45,47] or measured by the total station [48]. In Figure 1, 3D points P i (X wi , Y wi , Z wi ), i = 1, 2 in the world frame O_ZYZ_w are projected onto 2D image points p i (u i , v i ) on the camera image plane.…”

Section: Methodsmentioning

confidence: 95%

An Efficient Closed Form Solution to the Absolute Orientation Problem for Camera with Unknown Focal Length

Guo¹,

Hu²,

Zhao³

et al. 2021

Sensors

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In this paper we propose an efficient closed form solution to the absolute orientation problem for cameras with an unknown focal length, from two 2D–3D point correspondences and the camera position. The problem can be decomposed into two simple sub-problems and can be solved with angle constraints. A polynomial equation of one variable is solved to determine the focal length, and then a geometric approach is used to determine the absolute orientation. The geometric derivations are easy to understand and significantly improve performance. Rewriting the camera model with the known camera position leads to a simpler and more efficient closed form solution, and this gives a single solution, without the multi-solution phenomena of perspective-three-point (P3P) solvers. Experimental results demonstrated that our proposed method has a better performance in terms of numerical stability, noise sensitivity, and computational speed, with synthetic data and real images.

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“…The intrinsic parameters and pose parameters for the camera model were determined by the calibration technology. (15)(16)(17) The least-squares approach was applied to find a suitable solution using Eqs. ( 4) and ( 5).…”

Section: Derivation Of Modelmentioning

confidence: 99%

Machine-vision-based Spindle Positioning System of Grinding-wheel-saw Automatic Replacement System

Qiu¹,

Yang²,

Wu³

et al. 2022

Sensors and Materials

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To improve the working efficiency and safety of steel round bar sawing equipment, a grinding-wheel-saw automatic replacement system has been employed to replace grinding-wheel saws. In this paper, we report a machine-vision-based spindle positioning system for spindle localization in this replacement system. The hardware and software for the spindle positioning system were established, as well as an imaging model of the camera and the calibration of the vision system. The template matching approach applied to identify the shaft nut of the spindle is also described. Experimental results clearly demonstrate the effectiveness of the proposed machine-vision-based system.

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A Novel Method for Intrinsic and Extrinsic Parameters Estimation by Solving Perspective-Three-Point Problem with Known Camera Position

Cited by 15 publications

References 30 publications

An Accurate and Robust Method for Absolute Pose Estimation with UAV Using RANSAC

An Accurate and Robust Method for Absolute Pose Estimation with UAV Using RANSAC

An Efficient Closed Form Solution to the Absolute Orientation Problem for Camera with Unknown Focal Length

Machine-vision-based Spindle Positioning System of Grinding-wheel-saw Automatic Replacement System

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