Research on Calibration Method of Laser Camera Sensor
Laser camera sensor calibration is a key work step for a visual inspection system, and accurate solution of calibration model parameters is the difficulty of laser camera sensor calibration. In this paper, a laser camera sensor calibration mathematical model was established, and the process of solvi...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Control and Information Technology
2020-01-01
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| Series: | Kongzhi Yu Xinxi Jishu |
| Subjects: | |
| Online Access: | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2020.06.001 |
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| _version_ | 1849224870686097408 |
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| author | LIU Shiwang HU Yunqing LIN Jun |
| author_facet | LIU Shiwang HU Yunqing LIN Jun |
| author_sort | LIU Shiwang |
| collection | DOAJ |
| description | Laser camera sensor calibration is a key work step for a visual inspection system, and accurate solution of calibration model parameters is the difficulty of laser camera sensor calibration. In this paper, a laser camera sensor calibration mathematical model was established, and the process of solving the calibration model parameters by the nonlinear least square method and Gauss-Newton iterative method was analyzed, and a L-M algorithm based on maximum likelihood estimation was proposed. The algorithm uses the maximum likelihood estimation to reduce the error caused by image noise, and then the L-M method is used to find the optimal solution for the calibration parameters. Then, it used a needle-shaped target-based motion calibration method for calibration experiments. For 800 sets of calibration data, the nonlinear least square method, Gauss-Newton iteration method and the L-M algorithm based on maximum likelihood estimation were used to solve the calibration model parameters; the other 1 000 sets of calibration data were taken for error analysis. Comprehensive analysis of the two sets of data shows that mean square error of the L-M algorithm based on maximum likelihood estimation is reduced by 0.221 4 mm and 0.212 3 mm respectively, and the calibration accuracy of this method is improved effectively. |
| format | Article |
| id | doaj-art-4fc7e89aa5ee458ea0b74da139f7611d |
| institution | Kabale University |
| issn | 2096-5427 |
| language | zho |
| publishDate | 2020-01-01 |
| publisher | Editorial Office of Control and Information Technology |
| record_format | Article |
| series | Kongzhi Yu Xinxi Jishu |
| spelling | doaj-art-4fc7e89aa5ee458ea0b74da139f7611d2025-08-25T06:50:34ZzhoEditorial Office of Control and Information TechnologyKongzhi Yu Xinxi Jishu2096-54272020-01-0137182323613Research on Calibration Method of Laser Camera SensorLIU ShiwangHU YunqingLIN JunLaser camera sensor calibration is a key work step for a visual inspection system, and accurate solution of calibration model parameters is the difficulty of laser camera sensor calibration. In this paper, a laser camera sensor calibration mathematical model was established, and the process of solving the calibration model parameters by the nonlinear least square method and Gauss-Newton iterative method was analyzed, and a L-M algorithm based on maximum likelihood estimation was proposed. The algorithm uses the maximum likelihood estimation to reduce the error caused by image noise, and then the L-M method is used to find the optimal solution for the calibration parameters. Then, it used a needle-shaped target-based motion calibration method for calibration experiments. For 800 sets of calibration data, the nonlinear least square method, Gauss-Newton iteration method and the L-M algorithm based on maximum likelihood estimation were used to solve the calibration model parameters; the other 1 000 sets of calibration data were taken for error analysis. Comprehensive analysis of the two sets of data shows that mean square error of the L-M algorithm based on maximum likelihood estimation is reduced by 0.221 4 mm and 0.212 3 mm respectively, and the calibration accuracy of this method is improved effectively.http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2020.06.001laser camera sensorcalibration modelmaximum likelihood estimationL-M algorithm |
| spellingShingle | LIU Shiwang HU Yunqing LIN Jun Research on Calibration Method of Laser Camera Sensor Kongzhi Yu Xinxi Jishu laser camera sensor calibration model maximum likelihood estimation L-M algorithm |
| title | Research on Calibration Method of Laser Camera Sensor |
| title_full | Research on Calibration Method of Laser Camera Sensor |
| title_fullStr | Research on Calibration Method of Laser Camera Sensor |
| title_full_unstemmed | Research on Calibration Method of Laser Camera Sensor |
| title_short | Research on Calibration Method of Laser Camera Sensor |
| title_sort | research on calibration method of laser camera sensor |
| topic | laser camera sensor calibration model maximum likelihood estimation L-M algorithm |
| url | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2020.06.001 |
| work_keys_str_mv | AT liushiwang researchoncalibrationmethodoflasercamerasensor AT huyunqing researchoncalibrationmethodoflasercamerasensor AT linjun researchoncalibrationmethodoflasercamerasensor |