Optimization approach to solving pnp problem based on parameterization by rodrigues vector
- Authors: Abramenkov A.N.1
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Affiliations:
- V.A. Trapeznikov Institute of Control Sciences of RAS
- Issue: No 117 (2025)
- Pages: 200-219
- Section: Information technologies in control
- URL: https://journals.rcsi.science/1819-2440/article/view/360564
- DOI: https://doi.org/10.25728/ubs.2025.117.10
- ID: 360564
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Abstract
In many cases, the task of positioning a moving object using camera images can be reduced to well-known PnP (perspective-n-point) problem. For solving it, optimization algorithm is considered. The reprojection error using the parameterization of the rotation matrix by the Rodrigues vector is considered. Analytical partial derivatives were obtained for it. A feature of the task of positioning a moving object is that the new solution in most cases is in the neighborhood of the previous one. The main disadvantage of the optimization approach is the need to calculate the initial solution. It can be eliminated using the solution from the previous iteration. This also helps to eliminate the problem with the Rodrigues vector, which increases indefinitely as the rotation angle reaches 180 degrees. The BFGS numerical algorithm was used to solve the optimization problem. A heuristic for selecting the initial approximation of the inverse Hessian and the first step of the linear search is proposed. This made it possible to speed up the algorithm. The proposed approach is compared with known implementations from the OpnCV library on synthetic data. The experiment showed that the proposed approach has good performance in terms of accuracy and execution speed.
About the authors
Alexander Nikolaevich Abramenkov
V.A. Trapeznikov Institute of Control Sciences of RAS
Email: aabramenkov@asmon.ru
Moscow
References
1. БЕКЛЕМИШЕВ Н.Д. Прямое нахождение оценки поло-жения камеры центральной проекции по четырем опор-ным точкам // Математическое моделирование. – 2020. – Т. 32, №10. – С. 91–104.2. КУДИНОВ И.А., ПАВЛОВ О.В., ХОЛОПОВ И.С. Реали-зация алгоритма определения пространственных коор-динат и угловой ориентации объекта по реперным точ-кам, использующего информацию от одной камеры // Компьютерная оптика. – 2015. – Т. 39, №3. – С. 413–419.3. ХОЛОПОВ И.С., КАЛИНКИН А.И. Оценка погрешности определения угловых координат объекта с двумя репер-ными излучателями // Вестник Рязанского государствен-ного радиотехнического университета. – 2019. – №69. – С. 52–59.4. APARICIO-ESTEVE E., URENA J., HERNANDEZ A. et al. Using Perspective-n-Point Algorithms for a Local Position-ing System Based on LEDs and a QADA Receiver // Sensors. – 2021. – Vol. 21, No. 19. – 6537.5. CUI J., MIN C., FENG D. Research on pose estimation for stereo vision measurement system by an improved method: uncertainty weighted stereopsis pose solution method based on projection vector // Optics Express. – 2020. – Vol. 28, No. 4. – P. 5470–5491.6. DAI J.S. Euler–Rodrigues formula variations, quaternion conjugation and intrinsic connections // Mechanism and Machine Theory. – 2015. – Vol. 92. – P. 144–152.7. FISCHLER M.A., BOLLES R.C. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography // Communications of the ACM. – 1981. – Vol. 24, No. 6. – P. 381–395.8. FLETCHER R. Practical Methods of Optimization. – Wiley, 2000. – 436 p.9. GAO X.S., HOU X.R., TANG J. et al. Complete solution classification for the perspective-three-point problem // IEEE Trans. on Pattern Analysis and Machine Intelligence. – 2003. – Vol. 25, No. 8. – P. 930–943.10. HARTLEY R., ZISSERMAN A. Multiple View Geometry in Computer Vision. – New York: Cambridge University Press, 2003.11. HESCH J.A., ROUMELIOTIS S.I. A Direct Least-Squares (DLS) method for PnP // 2011 Int. Conf. on Computer Vi-sion. – 2011.12. Implementation of the BFGS algorithm in C++ [Электрон-ный ресурс]. – URL: https://github.com/IOdissey/bfgs (дата обращения: 27.03.2025).13. KAWECKI A., DĄBROWSKI P., JANUSZKO S. et al. AR Tags Based Absolute Positioning System // 8th Int. Conf. on Automation, Robotics and Applications (ICARA). – 2022. – P. 62–67.14. LEPETIT V., MORENO-NOGUER F., FUA P. EPnP: An Ac-curate O(n) Solution to the PnP Problem // Int. Journal of Computer Vision. – 2008. – Vol. 81. – P. 155–166.15. LV H., WU Q. An Energy-Efficient Field-Programmable Gate Array (FPGA) Implementation of a Real-Time Perspec-tive-n-Point Solver // Electronics. – 2024. – Vol. 13, No. 19. – 3815.16. Map-based navigation with ArUco markers [Электронный ресурс]. – URL: https://clover.coex.tech/en/aruco_map.html (дата обращения: 24.03.2025).17. NOCEDAL J., WRIGHT S.J. Numerical Optimization. – New York: Springer New York, 2006. – 664 p.18. OpenCV – Open Computer Vision Library [Электронный ресурс]. – URL: https://opencv.org/ (дата обращения 12.08.2025).19. QIAO R., XU G., WANG P. et al. An Accurate, Efficient, and Stable Perspective-n-Point Algorithm in 3D Space // Applied Sciences. – 2023. – Vol. 13, No. 2. – 1111.20. SCARAMUZZA D., FRAUNDORFER F. Visual Odometry [Tutorial] // IEEE Robotics & Automation Magazine. – 2011. – Vol. 18, No. 4. – P. 80–92.21. SUN Q., ZHANG T., ZHANG G. et al. Efficient Solution to PnP Problem Based on Vision Geometry // IEEE Robotics and Automation Letters. – 2024. – Vol. 9. No. 4. – P. 3100–3107.22. TERZAKIS G., LOURAKIS M. A Consistently Fast and Globally Optimal Solution to the Perspective-n-Point Prob-lem // European Conf. on Computer Vision. – 2020. – P. 478–494.23. VAKHITOV A., FERRAZ L., AGUDO Aet al. Uncertainty-aware camera pose estimation from points and lines // Proc. of IEEE/CVF Conf. on Computer Vision and Pattern Recog-nition (CVPR). – 2021. – P. 4659–4668.24. ZENG G., CHEN S., MU B. et al. CPnP: Consistent Pose Estimator for Perspective-n-Point Problem with Bias Elimi-nation // IEEE Int. Conf. on Robotics and Automation (ICRA). – 2023. – P. 1940–1946.25. ZHAN T., XU C., ZHANG C. et al. Generalized Maximum Likelihood Estimation for Perspective-n-Point Problem // IEEE Robotics and Automation Letters. – 2025. – Vol. 10, No. 2. – P. 1752–1759.26. ZHUANG S., ZHAO Z., CAO L. et al. A Robust and Fast Method to the Perspective-n-Point Problem for Camera Pose Estimation // IEEE Sensors Journal. – 2023. – Vol. 23, No. 11. – P. 11892–11906.
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