Registration of Multi-Sensor Bathymetric Point Clouds in Rural Areas Using Point-to-Grid Distances
This article proposes a method for registration of two different point clouds with different point densities and noise recorded by airborne sensors in rural areas. In particular, multi-sensor point clouds with different point densities are considered. The proposed method is marker-less and uses segm...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-04-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/8/4/178 |
| _version_ | 1831694124041895936 |
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| author | Richard Boerner Yusheng Xu Ramona Baran Frank Steinbacher Ludwig Hoegner Uwe Stilla |
| author_facet | Richard Boerner Yusheng Xu Ramona Baran Frank Steinbacher Ludwig Hoegner Uwe Stilla |
| author_sort | Richard Boerner |
| collection | DOAJ |
| description | This article proposes a method for registration of two different point clouds with different point densities and noise recorded by airborne sensors in rural areas. In particular, multi-sensor point clouds with different point densities are considered. The proposed method is marker-less and uses segmented ground areas for registration.Therefore, the proposed approach offers the possibility to fuse point clouds of different sensors in rural areas within an accuracy of fine registration. In general, such registration is solved with extensive use of control points. The source point cloud is used to calculate a DEM of the ground which is further used to calculate point to raster distances of all points of the target point cloud. Furthermore, each cell of the raster DEM gets a height variance, further addressed as reconstruction accuracy, by calculating the grid. An outlier removal based on a dynamic threshold of distances is used to gain more robustness against noise and small geometry variations. The transformation parameters are calculated with an iterative least-squares optimization of the distances weighted with respect to the reconstruction accuracies of the grid. Evaluations consider two flight campaigns of the Mangfall area inBavaria, Germany, taken with different airborne LiDAR sensors with different point density. The accuracy of the proposed approach is evaluated on the whole flight strip of approximately eight square kilometers as well as on selected scenes in a closer look. For all scenes, it obtained an accuracy of rotation parameters below one tenth degrees and accuracy of translation parameters below the point spacing and chosen cell size of the raster. Furthermore, the possibility of registration of airborne LiDAR and photogrammetric point clouds from UAV taken images is shown with a similar result. The evaluation also shows the robustness of the approach in scenes where a classical iterative closest point (ICP) fails. |
| first_indexed | 2024-12-20T12:28:30Z |
| format | Article |
| id | doaj.art-80f25432f27b43ecb1be60b0bb7b98b3 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-12-20T12:28:30Z |
| publishDate | 2019-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-80f25432f27b43ecb1be60b0bb7b98b32022-12-21T19:40:47ZengMDPI AGISPRS International Journal of Geo-Information2220-99642019-04-018417810.3390/ijgi8040178ijgi8040178Registration of Multi-Sensor Bathymetric Point Clouds in Rural Areas Using Point-to-Grid DistancesRichard Boerner0Yusheng Xu1Ramona Baran2Frank Steinbacher3Ludwig Hoegner4Uwe Stilla5Photogrammetry and Remote Sensing, Technical University of Munich, 80333 Munich, GermanyPhotogrammetry and Remote Sensing, Technical University of Munich, 80333 Munich, GermanySteinbacher Consult Ingenieurgesellschaft GmbH & Co. KG, Neusaess, 86356 Augsburg, GermanySteinbacher Consult Ingenieurgesellschaft GmbH & Co. KG, Neusaess, 86356 Augsburg, GermanyPhotogrammetry and Remote Sensing, Technical University of Munich, 80333 Munich, GermanyPhotogrammetry and Remote Sensing, Technical University of Munich, 80333 Munich, GermanyThis article proposes a method for registration of two different point clouds with different point densities and noise recorded by airborne sensors in rural areas. In particular, multi-sensor point clouds with different point densities are considered. The proposed method is marker-less and uses segmented ground areas for registration.Therefore, the proposed approach offers the possibility to fuse point clouds of different sensors in rural areas within an accuracy of fine registration. In general, such registration is solved with extensive use of control points. The source point cloud is used to calculate a DEM of the ground which is further used to calculate point to raster distances of all points of the target point cloud. Furthermore, each cell of the raster DEM gets a height variance, further addressed as reconstruction accuracy, by calculating the grid. An outlier removal based on a dynamic threshold of distances is used to gain more robustness against noise and small geometry variations. The transformation parameters are calculated with an iterative least-squares optimization of the distances weighted with respect to the reconstruction accuracies of the grid. Evaluations consider two flight campaigns of the Mangfall area inBavaria, Germany, taken with different airborne LiDAR sensors with different point density. The accuracy of the proposed approach is evaluated on the whole flight strip of approximately eight square kilometers as well as on selected scenes in a closer look. For all scenes, it obtained an accuracy of rotation parameters below one tenth degrees and accuracy of translation parameters below the point spacing and chosen cell size of the raster. Furthermore, the possibility of registration of airborne LiDAR and photogrammetric point clouds from UAV taken images is shown with a similar result. The evaluation also shows the robustness of the approach in scenes where a classical iterative closest point (ICP) fails.https://www.mdpi.com/2220-9964/8/4/178airborne point cloudsregistrationDEMmulti-sensor |
| spellingShingle | Richard Boerner Yusheng Xu Ramona Baran Frank Steinbacher Ludwig Hoegner Uwe Stilla Registration of Multi-Sensor Bathymetric Point Clouds in Rural Areas Using Point-to-Grid Distances ISPRS International Journal of Geo-Information airborne point clouds registration DEM multi-sensor |
| title | Registration of Multi-Sensor Bathymetric Point Clouds in Rural Areas Using Point-to-Grid Distances |
| title_full | Registration of Multi-Sensor Bathymetric Point Clouds in Rural Areas Using Point-to-Grid Distances |
| title_fullStr | Registration of Multi-Sensor Bathymetric Point Clouds in Rural Areas Using Point-to-Grid Distances |
| title_full_unstemmed | Registration of Multi-Sensor Bathymetric Point Clouds in Rural Areas Using Point-to-Grid Distances |
| title_short | Registration of Multi-Sensor Bathymetric Point Clouds in Rural Areas Using Point-to-Grid Distances |
| title_sort | registration of multi sensor bathymetric point clouds in rural areas using point to grid distances |
| topic | airborne point clouds registration DEM multi-sensor |
| url | https://www.mdpi.com/2220-9964/8/4/178 |
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