Monocular 3D Object Localization Using 2D Estimates for Industrial Robot Vision System
By: Thanh Nguyen Canh, Du Trinh Ngoc and Xiem HoangVan
References
- M. Salah, A. Ayyad, M. Humais, D. Gehrig, A. Abusafieh, L. Seneviratne, D. Scaramuzza, and Y. Zweiri, “E-calib: A fast, robust and accurate calibration toolbox for event cameras”, IEEE Transactions on Image Processing, vol. 33, 2024, 3977– 3990, doi: 10.1109/TIP.2024.3410673.
- Y. Cai, F. Wang, X. Wang, S. Li, Y. Wang, J. Yang, T. Yan, X. Zhan, F. Wang, R. Cheng, et al., “Broad-band visual adaption and image recognition in a monolithic neuromorphic machine vision system”, Advanced Functional Materials, vol. 33, no. 5, 2023, 2212917, doi: 10.1002/adfm.202 212917.
- M. Salah, M. Chehadah, M. Humais, M. Wahbah, A. Ayyad, R. Azzam, L. Seneviratne, and Y. Zweiri, “A neuromorphic vision-based measurement for robust relative localization in future space exploration missions”, IEEE Transactions on Instrumentation and Measurement, vol. 73, 2022, 1–12, doi: 10.1109/TIM.2022.3217513.
- L.-E. Montoya-Cavero, R. D. de León Torres, A. Gómez-Espinosa, and J. A. E. Cabello, “Vision systems for harvesting robots: Produce detection and localization”, Computers and electronics in agriculture, vol. 192, 2022, 106562, doi: 10.1 016/j.compag.2021.106562.
- N. Robinson, B. Tidd, D. Campbell, D. Kulić, and P. Corke, “Robotic vision for human-robot interaction and collaboration: A survey and systematic review”, ACM Transactions on Human-Robot Interaction, vol. 12, no. 1, 2023, 1–66, doi: 10.1 016/j.compag.2021.106562.
- P. Rosenberger, A. Cosgun, R. Newbury, J. Kwan, V. Ortenzi, P. Corke, and M. Grafinger, “Object-independent human-to-robot handovers using real time robotic vision”, IEEE Robotics and Automation Letters, vol. 6, no. 1, 2020, 17–23, doi: 10.1109/LRA.2020.3026970.
- G. D’Emilia and D. Di Gasbarro, “Review of techniques for 2d camera calibration suitable for industrial vision systems”. In: Journal of Physics: Conference Series, 2017, 012030, doi: 10.1088/1742-6596/841/1/012030.
- Y.-J. Zhang. “Camera calibration”. In: 3-D Computer Vision: Principles, Algorithms and Applications, 37–65. Springer, 2023.
- C. Ricolfe-Viala and A. Esparza, “The influence of autofocus lenses in the camera calibration process”, IEEE Transactions on Instrumentation and Measurement, vol. 70, 2021, 1–15, doi: 10.1109/TIM.2021.3055793.
- J.-H. Chuang, C.-H. Ho, A. Umam, H.-Y. Chen, J.-N. Hwang, and T.-A. Chen, “Geometry-based camera calibration using closed-form solution of principal line”, IEEE Transactions on Image Processing, vol. 30, 2021, 2599–2610, doi: 10.1109/TIP.20 20.3048684.
- S.-E. Lee, K. Shibata, S. Nonaka, S. Nobuhara, and K. Nishino, “Extrinsic camera calibration from a moving person”, IEEE Robotics and Automation Letters, vol. 7, no. 4, 2022, 10344–10351, doi: 10.1109/LRA.2022.3192629.
- B. Chen, Y. Liu, and C. Xiong, “Automatic checker-board detection for robust camera calibration”. In: 2021 IEEE International Conference on Multimedia and Expo (ICME), 2021, 1–6, doi: 10.110 9/ICME51207.2021.9428389.
- S. Liang and Y. Zhao, “Common pole-polar and common tangent properties of dual coplanar circles and their application in camera calibration”, Multimedia Tools and Applications, vol. 83, no. 1, 2024, 381–401, doi: 10. 1007/s 11042-023-15684-4.
- S. Liang and Y. Zhao, “Camera calibration based on the common pole-polar properties between two coplanar circles with various positions”, Applied Optics, vol. 59, no. 17, 2020, 5167–5178, doi: 10.1364/AO.388109.
- Y. Xie, R. Shao, P. Guli, B. Li, and L. Wang, “Infrastructure based calibration of a multi-camera and multi-lidar system using apriltags”. In: 2018 IEEE Intelligent Vehicles Symposium (IV), 2018, 605– 610, doi: 10.1109/IVS.2018.8500646.
- A. Wu, H. Xiao, and F. Zeng, “A camera calibration method based on opencv”. In: Proceedings of the 4th International Conference on Intelligent Information Processing, 2019, 320–324, doi: 10.1145/3378065.3378127.
- A. Fetić, D. Jurić, and D. Osmanković, “The procedure of a camera calibration using camera calibration toolbox for matlab”. In: 2012 Proceedings of the 35th International Convention MIPRO, 2012, 1752–1757.
- C. B. Duane, “Close-range camera calibration”, Photogramm. Eng, vol. 37, no. 8, 1971, 855–866.
- J. Fryer, T. Clarke, and J. Chen, “Lens distortion for simple c-mount lenses”, International Archives of Photogrammetry and remote sensing, vol. 30, 1994, 97–101.
- M.-T. Lu and J.-H. Chuang, “Fully automatic camera calibration for principal point using flat monitors”. In: 2018 25th IEEE International Conference on Image Processing (ICIP), 2018, 3154– 3158, doi: 10.1109/ICIP.2018.8451222.
- R. Tsai, “A versatile camera calibration technique for high-accuracy 3d machine vision metrology using off-the-shelf tv cameras and lenses”, IEEE Journal on Robotics and Automation, vol. 3, no. 4, 1987, 323–344, doi: 10.1109/JRA.1987.10871 09.
- M. Gašparović and D. Gajski, “Two-step camera calibration method developed for micro uav’s”, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 41, 2016, 829–833, doi: 10.5194/ispr s-archives-XLI-B1-829-2016.
- X. Chen, R. Fan, J. Wu, X. Song, Q. Liu, Y. Wang, Y. Wang, and B. Tao, “Fourier-transform-based two-stage camera calibration method with simple periodical pattern”, Optics and Lasers in Engineering, vol. 133, 2020, 106121, doi: 10.1016/j. optlaseng.2020.106121.
- Y. I. Abdel-Aziz, H. M. Karara, and M. Hauck, “Direct linear transformation from comparator coordinates into object space coordinates in close-range photogrammetry”, Photogrammetric engineering & remote sensing, vol. 81, no. 2, 2015, 103–107, doi: 10.14358/PERS.81.2.103.
- Z. Zhao, D. Ye, X. Zhang, G. Chen, and B. Zhang, “Improved direct linear transformation for parameter decoupling in camera calibration”, Algorithms, vol. 9, no. 2, 2016, 31, doi: 10.3390/a9020031.
- F. Barone, M. Marrazzo, and C. J. Oton, “Camera calibration with weighted direct linear transformation and anisotropic uncertainties of image control points”, Sensors, vol. 20, no. 4, 2020, 1175, doi: 10.3390/s20041175.
- Z. Shi, Y. Shang, X. Zhang, and G. Wang, “Dlt-lines based camera calibration with lens radial and tangential distortion”, Experimental Mechanics, vol. 61, no. 8, 2021, 1237–1247, doi: 10.1007/s11340-021-00726-5.
- Z. Zhang, “Flexible camera calibration by viewing a plane from unknown orientations”. In: Proceedings of the seventh ieee international conference on computer vision, 1999, 666–673, doi: 10.110 9/ICCV.1999.791289.
- T. H. M. Siddique, Y. Rehman, T. Rafiq, M. Z. Nisar, M. S. Ibrahim, and M. Usman, “3d object localization using 2d estimates for computer vision applications”. In: 2021 Mohammad Ali Jinnah University International Conference on Computing (MAJICC), 2021, 1–6, doi: 10.1109/MAJICC 53071.2021.9526270.
- X. H. Van and N. Do, “An efficient regression method for 3d object localization in machine vision systems”, IAES International Journal of Robotics and Automation, vol. 11, no. 2, 2022, 111, doi: 10.11591/ijra.v11i2.pp111-121.
- R. L. Van Hook. A Comparison of Monocular Camera Calibration Techniques. PhD thesis, Wright State University, 2014.
- J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection”. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, 779–788.
- J. Redmon and A. Farhadi, “Yolov3: An incremental improvement”, arXiv preprint arXiv:1804.02767, 2018.
- R. Girshick, “Fast r-cnn”. In: Proceedings of the IEEE international conference on computer vision, 2015, 1440–1448.
- A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, “Mobilenets: Efficient convolutional neural networks for mobile vision applications”, arXiv preprint arXiv:1704.04861, 2017.
- A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao, “Yolov4: Optimal speed and accuracy of object detection”, arXiv preprint arXiv:2004.10934, 2020.
- C. Lyu, W. Zhang, H. Huang, Y. Zhou, Y. Wang, Y. Liu, S. Zhang, and K. Chen, “Rtmdet: An empirical study of designing real-time object detectors”, arXiv preprint arXiv:2212.07784, 2022.
- C.-Y. Wang, A. Bochkovskiy, and H.-Y. M. Liao, “Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors”. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2023, 7464–7475.
- G. Jocher, A. Chaurasia, and J. Qiu. “Ultralytics yolov8”, January 2023.
- G. Jocher. “Ultralytics yolov5”, 2020.
- R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation”. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2014, 580–587.
Language: English
Page range: 53 - 65
Submitted on: Jun 13, 2024
Accepted on: Sep 13, 2024
Published on: Sep 10, 2025
Published by: Łukasiewicz Research Network – Industrial Research Institute for Automation and Measurements PIAP
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
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© 2025 Thanh Nguyen Canh, Du Trinh Ngoc, Xiem HoangVan, published by Łukasiewicz Research Network – Industrial Research Institute for Automation and Measurements PIAP
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.