Coarse Alignment Methodology of Point Cloud Based on Camera Position/Orientation Estimation Model
This study presents a methodology for the coarse alignment of light detection and ranging (LiDAR) point clouds, which involves estimating the position and orientation of each station using the pinhole camera model and a position/orientation estimation algorithm. Ground control points are obtained us...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/9/12/279 |
| _version_ | 1797380493746896896 |
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| author | Suhong Yoo Namhoon Kim |
| author_facet | Suhong Yoo Namhoon Kim |
| author_sort | Suhong Yoo |
| collection | DOAJ |
| description | This study presents a methodology for the coarse alignment of light detection and ranging (LiDAR) point clouds, which involves estimating the position and orientation of each station using the pinhole camera model and a position/orientation estimation algorithm. Ground control points are obtained using LiDAR camera images and the point clouds are obtained from the reference station. The estimated position and orientation vectors are used for point cloud registration. To evaluate the accuracy of the results, the positions of the LiDAR and the target were measured using a total station, and a comparison was carried out with the results of semi-automatic registration. The proposed methodology yielded an estimated mean LiDAR position error of 0.072 m, which was similar to the semi-automatic registration value of 0.070 m. When the point clouds of each station were registered using the estimated values, the mean registration accuracy was 0.124 m, while the semi-automatic registration accuracy was 0.072 m. The high accuracy of semi-automatic registration is due to its capability for performing both coarse alignment and refined registration. The comparison between the point cloud with refined alignment using the proposed methodology and the point-to-point distance analysis revealed that the average distance was measured at 0.0117 m. Moreover, 99% of the points exhibited distances within the range of 0.0696 m. |
| first_indexed | 2024-03-08T20:38:06Z |
| format | Article |
| id | doaj.art-9b25300bd23f46168793d909c96b375e |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-03-08T20:38:06Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-9b25300bd23f46168793d909c96b375e2023-12-22T14:18:13ZengMDPI AGJournal of Imaging2313-433X2023-12-0191227910.3390/jimaging9120279Coarse Alignment Methodology of Point Cloud Based on Camera Position/Orientation Estimation ModelSuhong Yoo0Namhoon Kim1Department of Drone and GIS Engineering, Namseoul University, 91, Daehak-ro, Seonghwan-eup, Seobuk-gu, Cheonan-si 31020, Republic of KoreaDepartment of Civil Engineering and Environmental Sciences, Korea Military Academy, 574, Hwarang-ro, Nowon-gu, Seoul 01805, Republic of KoreaThis study presents a methodology for the coarse alignment of light detection and ranging (LiDAR) point clouds, which involves estimating the position and orientation of each station using the pinhole camera model and a position/orientation estimation algorithm. Ground control points are obtained using LiDAR camera images and the point clouds are obtained from the reference station. The estimated position and orientation vectors are used for point cloud registration. To evaluate the accuracy of the results, the positions of the LiDAR and the target were measured using a total station, and a comparison was carried out with the results of semi-automatic registration. The proposed methodology yielded an estimated mean LiDAR position error of 0.072 m, which was similar to the semi-automatic registration value of 0.070 m. When the point clouds of each station were registered using the estimated values, the mean registration accuracy was 0.124 m, while the semi-automatic registration accuracy was 0.072 m. The high accuracy of semi-automatic registration is due to its capability for performing both coarse alignment and refined registration. The comparison between the point cloud with refined alignment using the proposed methodology and the point-to-point distance analysis revealed that the average distance was measured at 0.0117 m. Moreover, 99% of the points exhibited distances within the range of 0.0696 m.https://www.mdpi.com/2313-433X/9/12/279place recognitionpose estimationmappingsensor fusion for localizationLiDARcoarse alignment |
| spellingShingle | Suhong Yoo Namhoon Kim Coarse Alignment Methodology of Point Cloud Based on Camera Position/Orientation Estimation Model Journal of Imaging place recognition pose estimation mapping sensor fusion for localization LiDAR coarse alignment |
| title | Coarse Alignment Methodology of Point Cloud Based on Camera Position/Orientation Estimation Model |
| title_full | Coarse Alignment Methodology of Point Cloud Based on Camera Position/Orientation Estimation Model |
| title_fullStr | Coarse Alignment Methodology of Point Cloud Based on Camera Position/Orientation Estimation Model |
| title_full_unstemmed | Coarse Alignment Methodology of Point Cloud Based on Camera Position/Orientation Estimation Model |
| title_short | Coarse Alignment Methodology of Point Cloud Based on Camera Position/Orientation Estimation Model |
| title_sort | coarse alignment methodology of point cloud based on camera position orientation estimation model |
| topic | place recognition pose estimation mapping sensor fusion for localization LiDAR coarse alignment |
| url | https://www.mdpi.com/2313-433X/9/12/279 |
| work_keys_str_mv | AT suhongyoo coarsealignmentmethodologyofpointcloudbasedoncamerapositionorientationestimationmodel AT namhoonkim coarsealignmentmethodologyofpointcloudbasedoncamerapositionorientationestimationmodel |