{"ID":2889487,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.20516","arxiv_id":"2507.20516","title":"Large-Scale LiDAR-Inertial Dataset for Degradation-Robust High-Precision Mapping","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2507.20516","url_pdf":"https://arxiv.org/pdf/2507.20516v1","authors":"[\"Xiaofeng Jin\",\"Ningbo Bu\",\"Shijie Wang\",\"Jianfei Ge\",\"Jiangjian Xiao\",\"Matteo Matteucci\"]","published":"2025-07-28T04:38:37Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false}