Abstract
In order to improve the accuracy of conical target pose parameters measured by monocular vision, we propose a high precision visual measurement method for conical target based on the minimum reconstruction error of 3D. Firstly, we select the main and auxiliary features used for pose parameters’ measurement by analyzing target’s imaging process. Secondly, the algebraic and geometric methods for measuring pose parameters are determined based on the selected features. Finally, we utilize the geometric constraints of target to establish the reconstruction error formula and optimize the pose parameters based on the minimum reconstruction error principle to improve the measurement accuracy. To verify the effectiveness of our method, we use 3ds Max software to generate target’s simulation images under different pose parameters and compare the pose parameters before and after optimization. Experimental results show that the proposed method can significantly improve the accuracy of pose parameters when the measurement distance is far (more than 30 m), which is up to about 40%.
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ACKNOWLEDGMENTS
The author wishes to thank the anonymous reviewers for their valuable comments and the editors for their great efforts. It is the author’s responsibility for the article.
Funding
The study was supported by the National Natural Science Foundation of China, project no. 71701138.
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Xie Fei completed her Bachelor and Doctor degree in management from Beihang University, Beijing, China in the year 2006 and 2011. She is working as an Associate Professor in Capital University of Economics and Business, Beijing, China with the research interest in mathematical finance. She is a member of the grant “Research on multi-phase asset allocation optimization of pension funds based on stochastic programming theory” supported by the National Natural Science Foundation of China.
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Fei, X. A High Precision Conical Target Pose Measurement Method Using Monocular Vision. Pattern Recognit. Image Anal. 31, 595–600 (2021). https://doi.org/10.1134/S1054661821030081
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DOI: https://doi.org/10.1134/S1054661821030081