An Accurate, Efficient, and Stable Perspective-n-Point Algorithm in 3D Space
The Perspective-n-Point problem is usually addressed by means of a projective imaging model of 3D points, but the spatial distribution and quantity of 3D reference points vary, making it difficult for the Perspective-n-Point algorithm to balance accuracy, robustness, and computational efficiency. To...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/2/1111 |
| _version_ | 1797446513281990656 |
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| author | Rui Qiao Guili Xu Ping Wang Yuehua Cheng Wende Dong |
| author_facet | Rui Qiao Guili Xu Ping Wang Yuehua Cheng Wende Dong |
| author_sort | Rui Qiao |
| collection | DOAJ |
| description | The Perspective-n-Point problem is usually addressed by means of a projective imaging model of 3D points, but the spatial distribution and quantity of 3D reference points vary, making it difficult for the Perspective-n-Point algorithm to balance accuracy, robustness, and computational efficiency. To address this issue, this paper introduces Hidden PnP, a hidden variable method. Following the parameterization of the rotation matrix by CGR parameters, the method, unlike the existing best matrix synthesis technique (Gröbner technology), does not require construction of a larger matrix elimination template in the polynomial solution phase. Therefore, it is able to solve CGR parameter rapidly, and achieve an accurate location of the solution using the Gauss–Newton method. According to the synthetic data test, the PnP algorithm solution, based on hidden variables, outperforms the existing best Perspective-n-Point method in accuracy and robustness, under cases of Ordinary 3D, Planar Case, and Quasi-Singular. Furthermore, its computational efficiency can be up to seven times that of existing excellent algorithms when the spatially redundant reference points are increased to 500. In physical experiments on pose reprojection from monocular cameras, this algorithm even showed higher accuracy than the best existing algorithm. |
| first_indexed | 2024-03-09T13:41:34Z |
| format | Article |
| id | doaj.art-a17dae6430454c10ba816903cf1b85b9 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T13:41:34Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-a17dae6430454c10ba816903cf1b85b92023-11-30T21:06:21ZengMDPI AGApplied Sciences2076-34172023-01-01132111110.3390/app13021111An Accurate, Efficient, and Stable Perspective-n-Point Algorithm in 3D SpaceRui Qiao0Guili Xu1Ping Wang2Yuehua Cheng3Wende Dong4College of Automation, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, ChinaCollege of Automation, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, ChinaCollege of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, ChinaCollege of Automation, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, ChinaCollege of Automation, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, ChinaThe Perspective-n-Point problem is usually addressed by means of a projective imaging model of 3D points, but the spatial distribution and quantity of 3D reference points vary, making it difficult for the Perspective-n-Point algorithm to balance accuracy, robustness, and computational efficiency. To address this issue, this paper introduces Hidden PnP, a hidden variable method. Following the parameterization of the rotation matrix by CGR parameters, the method, unlike the existing best matrix synthesis technique (Gröbner technology), does not require construction of a larger matrix elimination template in the polynomial solution phase. Therefore, it is able to solve CGR parameter rapidly, and achieve an accurate location of the solution using the Gauss–Newton method. According to the synthetic data test, the PnP algorithm solution, based on hidden variables, outperforms the existing best Perspective-n-Point method in accuracy and robustness, under cases of Ordinary 3D, Planar Case, and Quasi-Singular. Furthermore, its computational efficiency can be up to seven times that of existing excellent algorithms when the spatially redundant reference points are increased to 500. In physical experiments on pose reprojection from monocular cameras, this algorithm even showed higher accuracy than the best existing algorithm.https://www.mdpi.com/2076-3417/13/2/1111Perspective-n-Point problemCGR parameter matrixGröbner technologyhidden PnP |
| spellingShingle | Rui Qiao Guili Xu Ping Wang Yuehua Cheng Wende Dong An Accurate, Efficient, and Stable Perspective-n-Point Algorithm in 3D Space Applied Sciences Perspective-n-Point problem CGR parameter matrix Gröbner technology hidden PnP |
| title | An Accurate, Efficient, and Stable Perspective-n-Point Algorithm in 3D Space |
| title_full | An Accurate, Efficient, and Stable Perspective-n-Point Algorithm in 3D Space |
| title_fullStr | An Accurate, Efficient, and Stable Perspective-n-Point Algorithm in 3D Space |
| title_full_unstemmed | An Accurate, Efficient, and Stable Perspective-n-Point Algorithm in 3D Space |
| title_short | An Accurate, Efficient, and Stable Perspective-n-Point Algorithm in 3D Space |
| title_sort | accurate efficient and stable perspective n point algorithm in 3d space |
| topic | Perspective-n-Point problem CGR parameter matrix Gröbner technology hidden PnP |
| url | https://www.mdpi.com/2076-3417/13/2/1111 |
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