构建大规模激光惯性数据集，支撑高精度地图在复杂环境下的鲁棒性评估。
Large-Scale LiDAR-Inertial Dataset for Degradation-Robust High-Precision Mapping
- 自建背负式平台采集多场景激光与惯性数据。
- 覆盖6万至75万平方米，轨迹长达数公里，精度达厘米级。
- 适合做高精度定位与导航系统性能测试的研究者使用。
本文提出一个大规模、高精度的激光-惯性里程计（LIO）数据集，旨在解决现有研究中对复杂真实场景下LIO系统验证不足的问题。数据集覆盖四个多样化的实际环境，面积介于6万至75万平方米之间，采用定制背负式平台采集，配备多波束激光雷达、工业级惯性测量单元（IMU）及RTK-GNSS模块。数据包含长距离轨迹、复杂场景以及通过融合基于SLAM的优化与RTK-GNSS锚定生成的高精度真值，并经由倾斜摄影测量与RTK-GNSS联合验证轨迹精度。该数据集为评估LIO系统在实际高精度地图构建中的泛化能力提供了全面基准。
原文摘要 · Abstract (English)
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.
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