Linear-Based Incremental Co-Registration of MLS and Photogrammetric Point Clouds
Today, mobile laser scanning and oblique photogrammetry are two standard urban remote sensing acquisition methods, and the cross-source point-cloud data obtained using these methods have significant differences and complementarity. Accurate co-registration can make up for the limitations of a single...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/13/11/2195 |
| _version_ | 1797531366603096064 |
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| author | Shiming Li Xuming Ge Shengfu Li Bo Xu Zhendong Wang |
| author_facet | Shiming Li Xuming Ge Shengfu Li Bo Xu Zhendong Wang |
| author_sort | Shiming Li |
| collection | DOAJ |
| description | Today, mobile laser scanning and oblique photogrammetry are two standard urban remote sensing acquisition methods, and the cross-source point-cloud data obtained using these methods have significant differences and complementarity. Accurate co-registration can make up for the limitations of a single data source, but many existing registration methods face critical challenges. Therefore, in this paper, we propose a systematic incremental registration method that can successfully register MLS and photogrammetric point clouds in the presence of a large number of missing data, large variations in point density, and scale differences. The robustness of this method is due to its elimination of noise in the extracted linear features and its 2D incremental registration strategy. There are three main contributions of our work: (1) the development of an end-to-end automatic cross-source point-cloud registration method; (2) a way to effectively extract the linear feature and restore the scale; and (3) an incremental registration strategy that simplifies the complex registration process. The experimental results show that this method can successfully achieve cross-source data registration, while other methods have difficulty obtaining satisfactory registration results efficiently. Moreover, this method can be extended to more point-cloud sources. |
| first_indexed | 2024-03-10T10:42:46Z |
| format | Article |
| id | doaj.art-399e3959226d4b05a4cf9c07f632d95c |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T10:42:46Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-399e3959226d4b05a4cf9c07f632d95c2023-11-21T22:49:18ZengMDPI AGRemote Sensing2072-42922021-06-011311219510.3390/rs13112195Linear-Based Incremental Co-Registration of MLS and Photogrammetric Point CloudsShiming Li0Xuming Ge1Shengfu Li2Bo Xu3Zhendong Wang4Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, ChinaFaculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, ChinaFaculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, ChinaFaculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, ChinaFaculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, ChinaToday, mobile laser scanning and oblique photogrammetry are two standard urban remote sensing acquisition methods, and the cross-source point-cloud data obtained using these methods have significant differences and complementarity. Accurate co-registration can make up for the limitations of a single data source, but many existing registration methods face critical challenges. Therefore, in this paper, we propose a systematic incremental registration method that can successfully register MLS and photogrammetric point clouds in the presence of a large number of missing data, large variations in point density, and scale differences. The robustness of this method is due to its elimination of noise in the extracted linear features and its 2D incremental registration strategy. There are three main contributions of our work: (1) the development of an end-to-end automatic cross-source point-cloud registration method; (2) a way to effectively extract the linear feature and restore the scale; and (3) an incremental registration strategy that simplifies the complex registration process. The experimental results show that this method can successfully achieve cross-source data registration, while other methods have difficulty obtaining satisfactory registration results efficiently. Moreover, this method can be extended to more point-cloud sources.https://www.mdpi.com/2072-4292/13/11/2195point-cloud registrationphotogrammetric point cloudMLS point cloudlinear featureincremental registration |
| spellingShingle | Shiming Li Xuming Ge Shengfu Li Bo Xu Zhendong Wang Linear-Based Incremental Co-Registration of MLS and Photogrammetric Point Clouds Remote Sensing point-cloud registration photogrammetric point cloud MLS point cloud linear feature incremental registration |
| title | Linear-Based Incremental Co-Registration of MLS and Photogrammetric Point Clouds |
| title_full | Linear-Based Incremental Co-Registration of MLS and Photogrammetric Point Clouds |
| title_fullStr | Linear-Based Incremental Co-Registration of MLS and Photogrammetric Point Clouds |
| title_full_unstemmed | Linear-Based Incremental Co-Registration of MLS and Photogrammetric Point Clouds |
| title_short | Linear-Based Incremental Co-Registration of MLS and Photogrammetric Point Clouds |
| title_sort | linear based incremental co registration of mls and photogrammetric point clouds |
| topic | point-cloud registration photogrammetric point cloud MLS point cloud linear feature incremental registration |
| url | https://www.mdpi.com/2072-4292/13/11/2195 |
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