Geo-Registration of Terrestrial LiDAR Point Clouds with Satellite Images without GNSS

• URL: http://arxiv.org/abs/2507.05999v2
• Date: Wed, 09 Jul 2025 04:44:50 GMT
• Title: Geo-Registration of Terrestrial LiDAR Point Clouds with Satellite Images without GNSS
• Authors: Xinyu Wang, Muhammad Ibrahim, Haitian Wang, Atif Mansoor, Ajmal Mian,
• Abstract summary: We propose a structured geo-registration and spatial correction method that aligns 3D point clouds with satellite images.<n>The proposed method was tested on the popular KITTI benchmark and a locally collected Perth (Western Australia) CBD dataset.<n>Our method achieved an average planimetric alignment standard deviation (STD) of 0.84m across sequences with intersections, representing a 55.3% improvement over the original dataset.
• Score: 25.05432314479669
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate geo-registration of LiDAR point clouds presents significant challenges in GNSS signal denied urban areas with high-rise buildings and bridges. Existing methods typically rely on real-time GNSS and IMU data, that require pre-calibration and assume stable positioning during data collection. However, this assumption often fails in dense urban areas, resulting in localization errors. To address this, we propose a structured geo-registration and spatial correction method that aligns 3D point clouds with satellite images, enabling frame-wise recovery of GNSS information and reconstruction of city scale 3D maps without relying on prior localization. The proposed approach employs a pre-trained Point Transformer model to segment the road points and then extracts the road skeleton and intersection points from the point cloud as well as the target map for alignment. Global rigid alignment of the two is performed using the intersection points, followed by local refinement using radial basis function (RBF) interpolation. Elevation correction is then applied to the point cloud based on terrain information from SRTM dataset to resolve vertical discrepancies. The proposed method was tested on the popular KITTI benchmark and a locally collected Perth (Western Australia) CBD dataset. On the KITTI dataset, our method achieved an average planimetric alignment standard deviation (STD) of 0.84~m across sequences with intersections, representing a 55.3\% improvement over the original dataset. On the Perth dataset, which lacks GNSS information, our method achieved an average STD of 0.96~m compared to the GPS data extracted from Google Maps API. This corresponds to a 77.4\% improvement from the initial alignment. Our method also resulted in elevation correlation gains of 30.5\% on the KITTI dataset and 50.4\% on the Perth dataset.

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