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.
Abstract (translated)
本文介绍了一个大规模、高精度的激光雷达-惯性里程计（LIO）数据集，旨在解决现有研究中针对复杂现实场景下LIO系统验证不足的问题。该数据集涵盖了四个不同的真实世界环境，面积从60,000到750,000平方米不等，使用配备有多线激光雷达、工业级惯性测量单元（IMU）和RTK-GNSS模块的定制背包平台采集而成。 数据集包含长轨迹、复杂场景以及高精度地面真实值，通过将SLAM优化与RTK-GNSS定位相结合生成，并通过倾斜摄影测量技术和RTK-GNSS对轨迹准确性进行了验证。该数据集为评估LIO系统在实际高精度地图绘制中的泛化能力提供了一个全面的基准。
URL
https://arxiv.org/abs/2507.20516
