Large-Scale LiDAR-Inertial Dataset for Degradation-Robust High-Precision Mapping
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866911079496417280 |
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| author | Jin, Xiaofeng Bu, Ningbo Wang, Shijie Ge, Jianfei Xiao, Jiangjian Matteucci, Matteo |
| author_facet | Jin, Xiaofeng Bu, Ningbo Wang, Shijie Ge, Jianfei Xiao, Jiangjian Matteucci, Matteo |
| contents | This paper introduces a large-scale, high-precision LiDAR-Inertial Odometry (LIO) dataset, aiming to address the insufficient validation of LIO systems in complex real-world scenarios in existing research. The dataset covers four diverse real-world environments spanning 60,000 to 750,000 square meters, collected using a custom backpack-mounted platform equipped with multi-beam LiDAR, an industrial-grade IMU, and RTK-GNSS modules. The dataset includes long trajectories, complex scenes, and high-precision ground truth, generated by fusing SLAM-based optimization with RTK-GNSS anchoring, and validated for trajectory accuracy through the integration of oblique photogrammetry and RTK-GNSS. This dataset provides a comprehensive benchmark for evaluating the generalization ability of LIO systems in practical high-precision mapping scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_20516 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Large-Scale LiDAR-Inertial Dataset for Degradation-Robust High-Precision Mapping Jin, Xiaofeng Bu, Ningbo Wang, Shijie Ge, Jianfei Xiao, Jiangjian Matteucci, Matteo Robotics 68T40 I.2.9 This paper introduces a large-scale, high-precision LiDAR-Inertial Odometry (LIO) dataset, aiming to address the insufficient validation of LIO systems in complex real-world scenarios in existing research. The dataset covers four diverse real-world environments spanning 60,000 to 750,000 square meters, collected using a custom backpack-mounted platform equipped with multi-beam LiDAR, an industrial-grade IMU, and RTK-GNSS modules. The dataset includes long trajectories, complex scenes, and high-precision ground truth, generated by fusing SLAM-based optimization with RTK-GNSS anchoring, and validated for trajectory accuracy through the integration of oblique photogrammetry and RTK-GNSS. This dataset provides a comprehensive benchmark for evaluating the generalization ability of LIO systems in practical high-precision mapping scenarios. |
| title | Large-Scale LiDAR-Inertial Dataset for Degradation-Robust High-Precision Mapping |
| topic | Robotics 68T40 I.2.9 |
| url | https://arxiv.org/abs/2507.20516 |