A Combined Physical and Mathematical Calibration Method for Low-Cost Cameras in the Air and Underwater Environment
Low-cost camera calibration is vital in air and underwater photogrammetric applications. However, various lens distortions and the underwater environment influence are difficult to be covered by a universal distortion compensation model, and the residual distortions may still remain after convention...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/4/2041 |
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| author | Zhenling Ma Xu Zhong Hong Xie Yongjun Zhou Yuan Chen Jiali Wang |
| author_facet | Zhenling Ma Xu Zhong Hong Xie Yongjun Zhou Yuan Chen Jiali Wang |
| author_sort | Zhenling Ma |
| collection | DOAJ |
| description | Low-cost camera calibration is vital in air and underwater photogrammetric applications. However, various lens distortions and the underwater environment influence are difficult to be covered by a universal distortion compensation model, and the residual distortions may still remain after conventional calibration. In this paper, we propose a combined physical and mathematical camera calibration method for low-cost cameras, which can adapt to both in-air and underwater environments. The commonly used physical distortion models are integrated to describe the image distortions. The combination is a high-order polynomial, which can be considered as basis functions to successively approximate the image deformation from the point of view of mathematical approximation. The calibration process is repeated until certain criteria are met and the distortions are reduced to a minimum. At the end, several sets of distortion parameters and stable camera interior orientation (IO) parameters act as the final camera calibration results. The Canon and GoPro in-air calibration experiments show that GoPro owns distortions seven times larger than Canon. Most Canon distortions have been described with the Australis model, while most decentering distortions for GoPro still exist. Using the proposed method, all the Canon and GoPro distortions are decreased to close to 0 after four calibrations. Meanwhile, the stable camera IO parameters are obtained. The GoPro Hero 5 Black underwater calibration indicates that four sets of distortion parameters and stable camera IO parameters are obtained after four calibrations. The camera calibration results show a difference between the underwater environment and air owing to the refractive and asymmetric environment effects. In summary, the proposed method improves the accuracy compared with the conventional method, which could be a flexible way to calibrate low-cost cameras for high accurate in-air and underwater measurement and 3D modeling applications. |
| first_indexed | 2024-03-11T08:10:46Z |
| format | Article |
| id | doaj.art-5ede77acf8e44c9088c4e3c62fa7e7b6 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T08:10:46Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-5ede77acf8e44c9088c4e3c62fa7e7b62023-11-16T23:09:32ZengMDPI AGSensors1424-82202023-02-01234204110.3390/s23042041A Combined Physical and Mathematical Calibration Method for Low-Cost Cameras in the Air and Underwater EnvironmentZhenling Ma0Xu Zhong1Hong Xie2Yongjun Zhou3Yuan Chen4Jiali Wang5College of Information Technology, Shanghai Ocean University, Shanghai 201306, ChinaSchool of Electronic and Information Engineering, Shanghai Dianji University, Shanghai 201306, ChinaSchool of Geodesy and Geomatics, Wuhan University, Wuhan 430079, ChinaSchool of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaCollege of Information Technology, Shanghai Ocean University, Shanghai 201306, ChinaCollege of Information Technology, Shanghai Ocean University, Shanghai 201306, ChinaLow-cost camera calibration is vital in air and underwater photogrammetric applications. However, various lens distortions and the underwater environment influence are difficult to be covered by a universal distortion compensation model, and the residual distortions may still remain after conventional calibration. In this paper, we propose a combined physical and mathematical camera calibration method for low-cost cameras, which can adapt to both in-air and underwater environments. The commonly used physical distortion models are integrated to describe the image distortions. The combination is a high-order polynomial, which can be considered as basis functions to successively approximate the image deformation from the point of view of mathematical approximation. The calibration process is repeated until certain criteria are met and the distortions are reduced to a minimum. At the end, several sets of distortion parameters and stable camera interior orientation (IO) parameters act as the final camera calibration results. The Canon and GoPro in-air calibration experiments show that GoPro owns distortions seven times larger than Canon. Most Canon distortions have been described with the Australis model, while most decentering distortions for GoPro still exist. Using the proposed method, all the Canon and GoPro distortions are decreased to close to 0 after four calibrations. Meanwhile, the stable camera IO parameters are obtained. The GoPro Hero 5 Black underwater calibration indicates that four sets of distortion parameters and stable camera IO parameters are obtained after four calibrations. The camera calibration results show a difference between the underwater environment and air owing to the refractive and asymmetric environment effects. In summary, the proposed method improves the accuracy compared with the conventional method, which could be a flexible way to calibrate low-cost cameras for high accurate in-air and underwater measurement and 3D modeling applications.https://www.mdpi.com/1424-8220/23/4/2041low-cost camera calibrationcombined physical distortion modelsmathematical approximationphotogrammetry |
| spellingShingle | Zhenling Ma Xu Zhong Hong Xie Yongjun Zhou Yuan Chen Jiali Wang A Combined Physical and Mathematical Calibration Method for Low-Cost Cameras in the Air and Underwater Environment Sensors low-cost camera calibration combined physical distortion models mathematical approximation photogrammetry |
| title | A Combined Physical and Mathematical Calibration Method for Low-Cost Cameras in the Air and Underwater Environment |
| title_full | A Combined Physical and Mathematical Calibration Method for Low-Cost Cameras in the Air and Underwater Environment |
| title_fullStr | A Combined Physical and Mathematical Calibration Method for Low-Cost Cameras in the Air and Underwater Environment |
| title_full_unstemmed | A Combined Physical and Mathematical Calibration Method for Low-Cost Cameras in the Air and Underwater Environment |
| title_short | A Combined Physical and Mathematical Calibration Method for Low-Cost Cameras in the Air and Underwater Environment |
| title_sort | combined physical and mathematical calibration method for low cost cameras in the air and underwater environment |
| topic | low-cost camera calibration combined physical distortion models mathematical approximation photogrammetry |
| url | https://www.mdpi.com/1424-8220/23/4/2041 |
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