Geo-Registration of Terrestrial LiDAR Point Clouds with Satellite Images without GNSS
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866917213394436096 |
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| author | Wang, Xinyu Ibrahim, Muhammad Wang, Haitian Mansoor, Atif Jia, Xiuping Mian, Ajmal |
| author_facet | Wang, Xinyu Ibrahim, Muhammad Wang, Haitian Mansoor, Atif Jia, Xiuping Mian, Ajmal |
| contents | Accurate geo-registration of LiDAR point clouds remains a significant challenge in urban environments where Global Navigation Satellite System (GNSS) signals are denied or degraded. Existing methods typically rely on real-time GNSS and Inertial Measurement Unit (IMU) data, which require pre-calibration and assume stable signals. However, this assumption often fails in dense cities, resulting in localization errors. To address this, we propose a structured post-hoc geo-registration method that accurately aligns LiDAR point clouds with satellite images. The proposed approach targets point cloud datasets where reliable GNSS information is unavailable or degraded, enabling city-scale geo-registration as a post-processing solution. Our method uses a pre-trained Point Transformer to segment road points, then extracts road skeletons and intersections from the point cloud and the satellite image. Global alignment is achieved through rigid transformation using corresponding intersection points, followed by local non-rigid refinement with radial basis function (RBF) interpolation. Elevation discrepancies are corrected using terrain data from the Shuttle Radar Topography Mission (SRTM). To evaluate geo-registration accuracy, we measure the absolute distances between the roads extracted from the two modalities. Our method is validated on the KITTI benchmark and a newly collected dataset of Perth, Western Australia. On KITTI, our method achieves a mean planimetric alignment error of 0.69m, corresponding to a 50% reduction in global geo-registration bias compared to the raw KITTI annotations. On Perth dataset, it achieves a mean planimetric error of 2.17m from GNSS values extracted from Google Maps, corresponding to 57.4% improvement over rigid alignment. Elevation correlation factor improved by 30.5% (KITTI) and 55.8% (Perth). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_05999 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Geo-Registration of Terrestrial LiDAR Point Clouds with Satellite Images without GNSS Wang, Xinyu Ibrahim, Muhammad Wang, Haitian Mansoor, Atif Jia, Xiuping Mian, Ajmal Computer Vision and Pattern Recognition Artificial Intelligence Accurate geo-registration of LiDAR point clouds remains a significant challenge in urban environments where Global Navigation Satellite System (GNSS) signals are denied or degraded. Existing methods typically rely on real-time GNSS and Inertial Measurement Unit (IMU) data, which require pre-calibration and assume stable signals. However, this assumption often fails in dense cities, resulting in localization errors. To address this, we propose a structured post-hoc geo-registration method that accurately aligns LiDAR point clouds with satellite images. The proposed approach targets point cloud datasets where reliable GNSS information is unavailable or degraded, enabling city-scale geo-registration as a post-processing solution. Our method uses a pre-trained Point Transformer to segment road points, then extracts road skeletons and intersections from the point cloud and the satellite image. Global alignment is achieved through rigid transformation using corresponding intersection points, followed by local non-rigid refinement with radial basis function (RBF) interpolation. Elevation discrepancies are corrected using terrain data from the Shuttle Radar Topography Mission (SRTM). To evaluate geo-registration accuracy, we measure the absolute distances between the roads extracted from the two modalities. Our method is validated on the KITTI benchmark and a newly collected dataset of Perth, Western Australia. On KITTI, our method achieves a mean planimetric alignment error of 0.69m, corresponding to a 50% reduction in global geo-registration bias compared to the raw KITTI annotations. On Perth dataset, it achieves a mean planimetric error of 2.17m from GNSS values extracted from Google Maps, corresponding to 57.4% improvement over rigid alignment. Elevation correlation factor improved by 30.5% (KITTI) and 55.8% (Perth). |
| title | Geo-Registration of Terrestrial LiDAR Point Clouds with Satellite Images without GNSS |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2507.05999 |