A Rigorous Feature Extraction Algorithm for Spherical Target Identification in Terrestrial Laser Scanning
Precise and rapid extraction of spherical target features from laser point clouds is critical for achieving high-precision registration of multiple point clouds. Existing methods often use linear models to represent spherical target characteristics, which have several drawbacks. This paper proposes...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/6/1491 |
| _version_ | 1827625850576568320 |
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| author | Ronghua Yang Jing Li Xiaolin Meng Yangsheng You |
| author_facet | Ronghua Yang Jing Li Xiaolin Meng Yangsheng You |
| author_sort | Ronghua Yang |
| collection | DOAJ |
| description | Precise and rapid extraction of spherical target features from laser point clouds is critical for achieving high-precision registration of multiple point clouds. Existing methods often use linear models to represent spherical target characteristics, which have several drawbacks. This paper proposes a rigorous estimation algorithm for spherical target features based on least squares configurations, in which the point-cloud data error is used as a random parameter, while the spherical center coordinates and radius are used as nonrandom parameters, emphasizing correlation between spherical parameters. The implementation details of this algorithm are illustrated by deriving calculation formulas for three variance–covariance matrices: variance–covariance matrices of the new observations, variance–covariance matrices of the new observation noise, and variance–covariance matrices of random parameters and the new observation noise. Experiments show that the estimation accuracy of sphere centers using our method is improved by at least 5.7% compared to classical algorithms, such as least squares, total least squares, and robust weighted total least squares. |
| first_indexed | 2024-03-09T12:44:29Z |
| format | Article |
| id | doaj.art-0612a326d75c4204953434c2ca6ed535 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T12:44:29Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-0612a326d75c4204953434c2ca6ed5352023-11-30T22:13:52ZengMDPI AGRemote Sensing2072-42922022-03-01146149110.3390/rs14061491A Rigorous Feature Extraction Algorithm for Spherical Target Identification in Terrestrial Laser ScanningRonghua Yang0Jing Li1Xiaolin Meng2Yangsheng You3School of Civil Engineering, Chongqing University, Chongqing 400045, ChinaSchool of Civil Engineering, Chongqing University, Chongqing 400045, ChinaCollege of Architecture and Civil Engineering, Beijing University of Technology, Beijing 100124, ChinaSchool of Civil Engineering, Chongqing University, Chongqing 400045, ChinaPrecise and rapid extraction of spherical target features from laser point clouds is critical for achieving high-precision registration of multiple point clouds. Existing methods often use linear models to represent spherical target characteristics, which have several drawbacks. This paper proposes a rigorous estimation algorithm for spherical target features based on least squares configurations, in which the point-cloud data error is used as a random parameter, while the spherical center coordinates and radius are used as nonrandom parameters, emphasizing correlation between spherical parameters. The implementation details of this algorithm are illustrated by deriving calculation formulas for three variance–covariance matrices: variance–covariance matrices of the new observations, variance–covariance matrices of the new observation noise, and variance–covariance matrices of random parameters and the new observation noise. Experiments show that the estimation accuracy of sphere centers using our method is improved by at least 5.7% compared to classical algorithms, such as least squares, total least squares, and robust weighted total least squares.https://www.mdpi.com/2072-4292/14/6/1491terrestrial laser scanningspherical center fittinglinear parameter estimationnonlinear parameter estimationleast squares configuration |
| spellingShingle | Ronghua Yang Jing Li Xiaolin Meng Yangsheng You A Rigorous Feature Extraction Algorithm for Spherical Target Identification in Terrestrial Laser Scanning Remote Sensing terrestrial laser scanning spherical center fitting linear parameter estimation nonlinear parameter estimation least squares configuration |
| title | A Rigorous Feature Extraction Algorithm for Spherical Target Identification in Terrestrial Laser Scanning |
| title_full | A Rigorous Feature Extraction Algorithm for Spherical Target Identification in Terrestrial Laser Scanning |
| title_fullStr | A Rigorous Feature Extraction Algorithm for Spherical Target Identification in Terrestrial Laser Scanning |
| title_full_unstemmed | A Rigorous Feature Extraction Algorithm for Spherical Target Identification in Terrestrial Laser Scanning |
| title_short | A Rigorous Feature Extraction Algorithm for Spherical Target Identification in Terrestrial Laser Scanning |
| title_sort | rigorous feature extraction algorithm for spherical target identification in terrestrial laser scanning |
| topic | terrestrial laser scanning spherical center fitting linear parameter estimation nonlinear parameter estimation least squares configuration |
| url | https://www.mdpi.com/2072-4292/14/6/1491 |
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