Toward Degradation-Robust High-Precision Mapping: A Large-Scale LiDAR–Inertial Dataset

LiDAR-Inertial Odometry (LIO) has demonstrated robust real-time capability and efficient mapping performance compared to traditional terrestrial laser scanners. Although recent advances driven by public datasets have improved LIO stability under certain degraded conditions, existing studies still lack systematic validation in real-world scenarios. Key challenges include long-duration sequences, combined degradation factors, and seamless indoor-outdoor transitions. This gap limits their applicability to general-purpose, high-precision mapping. To bridge this gap, we present a large-scale, high-precision LIO dataset collected in four diverse real-world environments, covering areas ranging from 60,000 to 750,000 m2. The dataset was collected with a custom backpack-mounted platform equipped with two multi-beam rotating LiDAR sensors, an industrial-grade IMU, and RTK-GNSS modules, achieving precise spatiotemporal alignment via hardware-level synchronization and rigorous calibration. The dataset encompasses a variety of environments, including structured buildings, tunnels, slopes, and urban scenes, to support seamless indoor-outdoor mapping. Each sequence has an average length of 1,500 m and comprises over 15,000 frames per sequence, exceeding the scale of most public LiDAR-IMU datasets. We also propose a 6-DoF ground-truth generation pipeline that fuses SLAM-based optimization with RTK-GNSS anchoring. Using oblique photogrammetry integrated with RTKGNSS measurements, we obtain georeferenced maps at a realworld scale. These maps are then used to align the LiDAR reconstructions and to validate the centimeter-level accuracy (< 3 cm) of the generated trajectories. This dataset advances the state of the art in trajectory length, scene complexity, and ground-truth precision, providing a comprehensive dataset for evaluating LIO systems and improving their generalization to practical high-precision mapping scenarios. We release the dataset, which can be obtained from the following link: https: //github.com/CNITECH-CV-LAB/Backpack2025