Imagery Network Fine Registration by Reference Point Cloud Data Based on the Tie Points and Planes
Cameras and laser scanners are complementary tools for a 2D/3D information generation. Systematic and random errors cause the misalignment of the multi-sensor imagery and point cloud data. In this paper, a novel feature-based approach is proposed for imagery and point cloud fine registration. The ti...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-01-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/1/317 |
| _version_ | 1797542273525743616 |
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| author | Mehrdad Eslami Mohammad Saadatseresht |
| author_facet | Mehrdad Eslami Mohammad Saadatseresht |
| author_sort | Mehrdad Eslami |
| collection | DOAJ |
| description | Cameras and laser scanners are complementary tools for a 2D/3D information generation. Systematic and random errors cause the misalignment of the multi-sensor imagery and point cloud data. In this paper, a novel feature-based approach is proposed for imagery and point cloud fine registration. The tie points and its two neighbor pixels are matched in the overlap images, which are intersected in the object space to create the differential tie plane. A preprocessing is applied to the corresponding tie points and non-robust ones are removed. Initial coarse Exterior Orientation Parameters (EOPs), Interior Orientation Parameters (IOPs), and Additional Parameters (APs) are used to transform tie plane points to the object space. Then, the nearest points of the point cloud data to the transformed tie plane points are estimated. These estimated points are used to calculate Directional Vectors (DV) of the differential planes. As a constraint equation along with the collinearity equation, each object space tie point is forced to be located on the point cloud differential plane. Two different indoor and outdoor experimental data are used to assess the proposed approach. Achieved results show about 2.5 pixels errors on checkpoints. Such results demonstrated the robustness and practicality of the proposed approach. |
| first_indexed | 2024-03-10T13:28:17Z |
| format | Article |
| id | doaj.art-a8b722a6f4084b60ace13fa77ddeee11 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T13:28:17Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-a8b722a6f4084b60ace13fa77ddeee112023-11-21T08:33:43ZengMDPI AGSensors1424-82202021-01-0121131710.3390/s21010317Imagery Network Fine Registration by Reference Point Cloud Data Based on the Tie Points and PlanesMehrdad Eslami0Mohammad Saadatseresht1School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran 1439957131, IranSchool of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran 1439957131, IranCameras and laser scanners are complementary tools for a 2D/3D information generation. Systematic and random errors cause the misalignment of the multi-sensor imagery and point cloud data. In this paper, a novel feature-based approach is proposed for imagery and point cloud fine registration. The tie points and its two neighbor pixels are matched in the overlap images, which are intersected in the object space to create the differential tie plane. A preprocessing is applied to the corresponding tie points and non-robust ones are removed. Initial coarse Exterior Orientation Parameters (EOPs), Interior Orientation Parameters (IOPs), and Additional Parameters (APs) are used to transform tie plane points to the object space. Then, the nearest points of the point cloud data to the transformed tie plane points are estimated. These estimated points are used to calculate Directional Vectors (DV) of the differential planes. As a constraint equation along with the collinearity equation, each object space tie point is forced to be located on the point cloud differential plane. Two different indoor and outdoor experimental data are used to assess the proposed approach. Achieved results show about 2.5 pixels errors on checkpoints. Such results demonstrated the robustness and practicality of the proposed approach.https://www.mdpi.com/1424-8220/21/1/317fine registrationphotogrammetric imagerylaser scanner point cloudmobile mapping systemscalibration |
| spellingShingle | Mehrdad Eslami Mohammad Saadatseresht Imagery Network Fine Registration by Reference Point Cloud Data Based on the Tie Points and Planes Sensors fine registration photogrammetric imagery laser scanner point cloud mobile mapping systems calibration |
| title | Imagery Network Fine Registration by Reference Point Cloud Data Based on the Tie Points and Planes |
| title_full | Imagery Network Fine Registration by Reference Point Cloud Data Based on the Tie Points and Planes |
| title_fullStr | Imagery Network Fine Registration by Reference Point Cloud Data Based on the Tie Points and Planes |
| title_full_unstemmed | Imagery Network Fine Registration by Reference Point Cloud Data Based on the Tie Points and Planes |
| title_short | Imagery Network Fine Registration by Reference Point Cloud Data Based on the Tie Points and Planes |
| title_sort | imagery network fine registration by reference point cloud data based on the tie points and planes |
| topic | fine registration photogrammetric imagery laser scanner point cloud mobile mapping systems calibration |
| url | https://www.mdpi.com/1424-8220/21/1/317 |
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