A New Tie Plane-Based Method for Fine Registration of Imagery and Point Cloud Dataset
Today, both point cloud and imagery datasets processed for mapping aims. The precise fusion of both datasets is a major issue that leads to the fine registration problem. This article proposes a fine registration method based on a novel concept of tie plane. The assumption of our solution is that th...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2020-05-01
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| Series: | Canadian Journal of Remote Sensing |
| Online Access: | http://dx.doi.org/10.1080/07038992.2020.1785282 |
| _version_ | 1797661110131752960 |
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| author | Mehrdad Eslami Mohammad Saadatseresht |
| author_facet | Mehrdad Eslami Mohammad Saadatseresht |
| author_sort | Mehrdad Eslami |
| collection | DOAJ |
| description | Today, both point cloud and imagery datasets processed for mapping aims. The precise fusion of both datasets is a major issue that leads to the fine registration problem. This article proposes a fine registration method based on a novel concept of tie plane. The assumption of our solution is that the laser scanner point cloud is much more accurate than the image interior and exterior geometric accuracy. In fact, we register the inaccurate image network to the accurate point cloud data. To do this, tie points are extracted from images. Then, the fine registration is commenced by filtering the unstable tie points as the preprocessing phase. Subsequently, tie planes are reconstructed around the remaining tie points by photogrammetric space intersection. The tie planes are locally fitted to the point cloud data via both normal and directional vectors. Afterward, a novel combined bundle adjustment is developed based on the conventional tie point equations and the new tie plane constraints. Therefore, the interior and exterior orientation parameters are refined. To evaluate our solution, both indoor and outdoor datasets are experimented. The results illustrate a registration error of about <1.6 pixels for both datasets, indicating ∼23% to 40% average accuracy improvement compared to the existing methods. |
| first_indexed | 2024-03-11T18:40:31Z |
| format | Article |
| id | doaj.art-4be70692d8ca4885a8e8afe8127839fd |
| institution | Directory Open Access Journal |
| issn | 1712-7971 |
| language | English |
| last_indexed | 2024-03-11T18:40:31Z |
| publishDate | 2020-05-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Canadian Journal of Remote Sensing |
| spelling | doaj.art-4be70692d8ca4885a8e8afe8127839fd2023-10-12T13:36:23ZengTaylor & Francis GroupCanadian Journal of Remote Sensing1712-79712020-05-0146329531210.1080/07038992.2020.17852821785282A New Tie Plane-Based Method for Fine Registration of Imagery and Point Cloud DatasetMehrdad Eslami0Mohammad Saadatseresht1School of Surveying and Geospatial Engineering, College of Engineering, University of TehranSchool of Surveying and Geospatial Engineering, College of Engineering, University of TehranToday, both point cloud and imagery datasets processed for mapping aims. The precise fusion of both datasets is a major issue that leads to the fine registration problem. This article proposes a fine registration method based on a novel concept of tie plane. The assumption of our solution is that the laser scanner point cloud is much more accurate than the image interior and exterior geometric accuracy. In fact, we register the inaccurate image network to the accurate point cloud data. To do this, tie points are extracted from images. Then, the fine registration is commenced by filtering the unstable tie points as the preprocessing phase. Subsequently, tie planes are reconstructed around the remaining tie points by photogrammetric space intersection. The tie planes are locally fitted to the point cloud data via both normal and directional vectors. Afterward, a novel combined bundle adjustment is developed based on the conventional tie point equations and the new tie plane constraints. Therefore, the interior and exterior orientation parameters are refined. To evaluate our solution, both indoor and outdoor datasets are experimented. The results illustrate a registration error of about <1.6 pixels for both datasets, indicating ∼23% to 40% average accuracy improvement compared to the existing methods.http://dx.doi.org/10.1080/07038992.2020.1785282 |
| spellingShingle | Mehrdad Eslami Mohammad Saadatseresht A New Tie Plane-Based Method for Fine Registration of Imagery and Point Cloud Dataset Canadian Journal of Remote Sensing |
| title | A New Tie Plane-Based Method for Fine Registration of Imagery and Point Cloud Dataset |
| title_full | A New Tie Plane-Based Method for Fine Registration of Imagery and Point Cloud Dataset |
| title_fullStr | A New Tie Plane-Based Method for Fine Registration of Imagery and Point Cloud Dataset |
| title_full_unstemmed | A New Tie Plane-Based Method for Fine Registration of Imagery and Point Cloud Dataset |
| title_short | A New Tie Plane-Based Method for Fine Registration of Imagery and Point Cloud Dataset |
| title_sort | new tie plane based method for fine registration of imagery and point cloud dataset |
| url | http://dx.doi.org/10.1080/07038992.2020.1785282 |
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