A General Point-Based Method for Self-Calibration of Terrestrial Laser Scanners Considering Stochastic Information
Due to the existence of environmental or human factors, and because of the instrument itself, there are many uncertainties in point clouds, which directly affect the data quality and the accuracy of subsequent processing, such as point cloud segmentation, 3D modeling, etc. In this paper, to address...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-09-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/18/2923 |
| _version_ | 1797554113641185280 |
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| author | Tengfei Zhou Xiaojun Cheng Peng Lin Zhenlun Wu Ensheng Liu |
| author_facet | Tengfei Zhou Xiaojun Cheng Peng Lin Zhenlun Wu Ensheng Liu |
| author_sort | Tengfei Zhou |
| collection | DOAJ |
| description | Due to the existence of environmental or human factors, and because of the instrument itself, there are many uncertainties in point clouds, which directly affect the data quality and the accuracy of subsequent processing, such as point cloud segmentation, 3D modeling, etc. In this paper, to address this problem, stochastic information of point cloud coordinates is taken into account, and on the basis of the scanner observation principle within the Gauss–Helmert model, a novel general point-based self-calibration method is developed for terrestrial laser scanners, incorporating both five additional parameters and six exterior orientation parameters. For cases where the instrument accuracy is different from the nominal ones, the variance component estimation algorithm is implemented for reweighting the outliers after the residual errors of observations obtained. Considering that the proposed method essentially is a nonlinear model, the Gauss–Newton iteration method is applied to derive the solutions of additional parameters and exterior orientation parameters. We conducted experiments using simulated and real data and compared them with those two existing methods. The experimental results showed that the proposed method could improve the point accuracy from 10<sup>−4</sup> to 10<sup>−8</sup> (a priori known) and 10<sup>−7</sup> (a priori unknown), and reduced the correlation among the parameters (approximately 60% of volume). However, it is undeniable that some correlations increased instead, which is the limitation of the general method. |
| first_indexed | 2024-03-10T16:27:07Z |
| format | Article |
| id | doaj.art-1f96d5887887417592dbe057f35fd105 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T16:27:07Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-1f96d5887887417592dbe057f35fd1052023-11-20T13:06:37ZengMDPI AGRemote Sensing2072-42922020-09-011218292310.3390/rs12182923A General Point-Based Method for Self-Calibration of Terrestrial Laser Scanners Considering Stochastic InformationTengfei Zhou0Xiaojun Cheng1Peng Lin2Zhenlun Wu3Ensheng Liu4College of Survey and Geo-Informatics, Tongji University, Shanghai 200092, ChinaCollege of Survey and Geo-Informatics, Tongji University, Shanghai 200092, ChinaCollege of Civil Engineering, Anhui Jianzhu University, Hefei 232001, ChinaBig Data Development Administration of Yichun, Yichun 336000, ChinaCollege of Survey and Geo-Informatics, Tongji University, Shanghai 200092, ChinaDue to the existence of environmental or human factors, and because of the instrument itself, there are many uncertainties in point clouds, which directly affect the data quality and the accuracy of subsequent processing, such as point cloud segmentation, 3D modeling, etc. In this paper, to address this problem, stochastic information of point cloud coordinates is taken into account, and on the basis of the scanner observation principle within the Gauss–Helmert model, a novel general point-based self-calibration method is developed for terrestrial laser scanners, incorporating both five additional parameters and six exterior orientation parameters. For cases where the instrument accuracy is different from the nominal ones, the variance component estimation algorithm is implemented for reweighting the outliers after the residual errors of observations obtained. Considering that the proposed method essentially is a nonlinear model, the Gauss–Newton iteration method is applied to derive the solutions of additional parameters and exterior orientation parameters. We conducted experiments using simulated and real data and compared them with those two existing methods. The experimental results showed that the proposed method could improve the point accuracy from 10<sup>−4</sup> to 10<sup>−8</sup> (a priori known) and 10<sup>−7</sup> (a priori unknown), and reduced the correlation among the parameters (approximately 60% of volume). However, it is undeniable that some correlations increased instead, which is the limitation of the general method.https://www.mdpi.com/2072-4292/12/18/2923self-calibrationGauss–Helmert modelrandom errorGauss–Newton methodvariance component estimation |
| spellingShingle | Tengfei Zhou Xiaojun Cheng Peng Lin Zhenlun Wu Ensheng Liu A General Point-Based Method for Self-Calibration of Terrestrial Laser Scanners Considering Stochastic Information Remote Sensing self-calibration Gauss–Helmert model random error Gauss–Newton method variance component estimation |
| title | A General Point-Based Method for Self-Calibration of Terrestrial Laser Scanners Considering Stochastic Information |
| title_full | A General Point-Based Method for Self-Calibration of Terrestrial Laser Scanners Considering Stochastic Information |
| title_fullStr | A General Point-Based Method for Self-Calibration of Terrestrial Laser Scanners Considering Stochastic Information |
| title_full_unstemmed | A General Point-Based Method for Self-Calibration of Terrestrial Laser Scanners Considering Stochastic Information |
| title_short | A General Point-Based Method for Self-Calibration of Terrestrial Laser Scanners Considering Stochastic Information |
| title_sort | general point based method for self calibration of terrestrial laser scanners considering stochastic information |
| topic | self-calibration Gauss–Helmert model random error Gauss–Newton method variance component estimation |
| url | https://www.mdpi.com/2072-4292/12/18/2923 |
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