IMPROVING GPS TRAJECTORIES USING 3D CITY MODELS AND KINEMATIC POINT CLOUDS
Accurate and robust positioning of vehicles in urban environments is of high importance for many applications (e.g. autonomous driving or mobile mapping). In the case of mobile mapping systems, a simultaneous mapping of the environment using laser scanning and an accurate positioning using GNSS is t...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2019-09-01
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| Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-4-W9/35/2019/isprs-annals-IV-4-W9-35-2019.pdf |
| _version_ | 1818922686447878144 |
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| author | Y. Dehbi L. Lucks J. Behmann L. Klingbeil L. Plümer |
| author_facet | Y. Dehbi L. Lucks J. Behmann L. Klingbeil L. Plümer |
| author_sort | Y. Dehbi |
| collection | DOAJ |
| description | Accurate and robust positioning of vehicles in urban environments is of high importance for many applications (e.g. autonomous driving or mobile mapping). In the case of mobile mapping systems, a simultaneous mapping of the environment using laser scanning and an accurate positioning using GNSS is targeted. This requirement is often not guaranteed in shadowed cities where GNSS signals are usually disturbed, weak or even unavailable. Both, the generated point clouds and the derived trajectory are consequently imprecise. We propose a novel approach which incorporates prior knowledge, i.e. 3D building model of the environment, and improves the point cloud and the trajectory. The key idea is to benefit from the complementarity of both GNSS and 3D building models. The point cloud is matched to the city model using a point-to-plane ICP. An informed sampling of appropriate matching points is enabled by a pre-classification step. Support vector machines (SVMs) are used to discriminate between facade and remaining points. Local inconsistencies are tackled by a segment-wise partitioning of the point cloud where an interpolation guarantees a seamless transition between the segments. The full processing chain is implemented from the detection of facades in the point clouds, the matching between them and the building models and the update of the trajectory estimate. The general applicability of the implemented method is demonstrated on an inner city data set recorded with a mobile mapping system. |
| first_indexed | 2024-12-20T01:57:29Z |
| format | Article |
| id | doaj.art-1fa5b514317a45a890500ef061fe8536 |
| institution | Directory Open Access Journal |
| issn | 2194-9042 2194-9050 |
| language | English |
| last_indexed | 2024-12-20T01:57:29Z |
| publishDate | 2019-09-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-1fa5b514317a45a890500ef061fe85362022-12-21T19:57:26ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502019-09-01IV-4-W9354210.5194/isprs-annals-IV-4-W9-35-2019IMPROVING GPS TRAJECTORIES USING 3D CITY MODELS AND KINEMATIC POINT CLOUDSY. Dehbi0L. Lucks1J. Behmann2L. Klingbeil3L. Plümer4Institute of Geodesy and Geoinformation, University of Bonn, GermanyFraunhofer (IOSB) Institute of Optronics, System Technologies and Image Exploitation, GermanyInstitute of Crop Sciences and Resource Protection, University of Bonn, GermanyInstitute of Geodesy and Geoinformation, University of Bonn, GermanyFaculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, ChinaAccurate and robust positioning of vehicles in urban environments is of high importance for many applications (e.g. autonomous driving or mobile mapping). In the case of mobile mapping systems, a simultaneous mapping of the environment using laser scanning and an accurate positioning using GNSS is targeted. This requirement is often not guaranteed in shadowed cities where GNSS signals are usually disturbed, weak or even unavailable. Both, the generated point clouds and the derived trajectory are consequently imprecise. We propose a novel approach which incorporates prior knowledge, i.e. 3D building model of the environment, and improves the point cloud and the trajectory. The key idea is to benefit from the complementarity of both GNSS and 3D building models. The point cloud is matched to the city model using a point-to-plane ICP. An informed sampling of appropriate matching points is enabled by a pre-classification step. Support vector machines (SVMs) are used to discriminate between facade and remaining points. Local inconsistencies are tackled by a segment-wise partitioning of the point cloud where an interpolation guarantees a seamless transition between the segments. The full processing chain is implemented from the detection of facades in the point clouds, the matching between them and the building models and the update of the trajectory estimate. The general applicability of the implemented method is demonstrated on an inner city data set recorded with a mobile mapping system.https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-4-W9/35/2019/isprs-annals-IV-4-W9-35-2019.pdf |
| spellingShingle | Y. Dehbi L. Lucks J. Behmann L. Klingbeil L. Plümer IMPROVING GPS TRAJECTORIES USING 3D CITY MODELS AND KINEMATIC POINT CLOUDS ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | IMPROVING GPS TRAJECTORIES USING 3D CITY MODELS AND KINEMATIC POINT CLOUDS |
| title_full | IMPROVING GPS TRAJECTORIES USING 3D CITY MODELS AND KINEMATIC POINT CLOUDS |
| title_fullStr | IMPROVING GPS TRAJECTORIES USING 3D CITY MODELS AND KINEMATIC POINT CLOUDS |
| title_full_unstemmed | IMPROVING GPS TRAJECTORIES USING 3D CITY MODELS AND KINEMATIC POINT CLOUDS |
| title_short | IMPROVING GPS TRAJECTORIES USING 3D CITY MODELS AND KINEMATIC POINT CLOUDS |
| title_sort | improving gps trajectories using 3d city models and kinematic point clouds |
| url | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-4-W9/35/2019/isprs-annals-IV-4-W9-35-2019.pdf |
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