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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.07362 |
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| _version_ | 1866915485999693824 |
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| author | Yang, Yandi Li, Jianping Liao, Youqi Li, Yuhao Zhang, Yizhe Dong, Zhen Yang, Bisheng El-Sheimy, Naser |
| author_facet | Yang, Yandi Li, Jianping Liao, Youqi Li, Yuhao Zhang, Yizhe Dong, Zhen Yang, Bisheng El-Sheimy, Naser |
| contents | Accurate visual localization in dense urban environments poses a fundamental task in photogrammetry, geospatial information science, and robotics. While imagery is a low-cost and widely accessible sensing modality, its effectiveness on visual odometry is often limited by textureless surfaces, severe viewpoint changes, and long-term drift. The growing public availability of airborne laser scanning (ALS) data opens new avenues for scalable and precise visual localization by leveraging ALS as a prior map. However, the potential of ALS-based localization remains underexplored due to three key limitations: (1) the lack of platform-diverse datasets, (2) the absence of reliable ground-truth generation methods applicable to large-scale urban environments, and (3) limited validation of existing Image-to-Point Cloud (I2P) algorithms under aerial-ground cross-platform settings. To overcome these challenges, we introduce a new large-scale dataset that integrates ground-level imagery from mobile mapping systems with ALS point clouds collected in Wuhan, Hong Kong, and San Francisco. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_07362 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Aerial-ground Cross-modal Localization: Dataset, Ground-truth, and Benchmark Yang, Yandi Li, Jianping Liao, Youqi Li, Yuhao Zhang, Yizhe Dong, Zhen Yang, Bisheng El-Sheimy, Naser Robotics Accurate visual localization in dense urban environments poses a fundamental task in photogrammetry, geospatial information science, and robotics. While imagery is a low-cost and widely accessible sensing modality, its effectiveness on visual odometry is often limited by textureless surfaces, severe viewpoint changes, and long-term drift. The growing public availability of airborne laser scanning (ALS) data opens new avenues for scalable and precise visual localization by leveraging ALS as a prior map. However, the potential of ALS-based localization remains underexplored due to three key limitations: (1) the lack of platform-diverse datasets, (2) the absence of reliable ground-truth generation methods applicable to large-scale urban environments, and (3) limited validation of existing Image-to-Point Cloud (I2P) algorithms under aerial-ground cross-platform settings. To overcome these challenges, we introduce a new large-scale dataset that integrates ground-level imagery from mobile mapping systems with ALS point clouds collected in Wuhan, Hong Kong, and San Francisco. |
| title | Aerial-ground Cross-modal Localization: Dataset, Ground-truth, and Benchmark |
| topic | Robotics |
| url | https://arxiv.org/abs/2509.07362 |