LiDAR-based 3D mapping suffers from cumulative drift causing global misalignment, particularly in GNSS-constrained environments. To address this, we propose a unified framework that fuses LiDAR, GNSS, and IMU data for high-resolution city-scale mapping. The method performs velocity-based temporal alignment using Dynamic Time Warping and refines GNSS and IMU signals via extended Kalman filtering. Local maps are built using Normal Distributions Transform-based registration and pose graph optimization with loop closure detection, while global consistency is enforced using GNSS-constrained anchors followed by fine registration of overlapping segments. We also introduce a large-scale multimodal dataset captured in Perth, Western Australia to facilitate future research in this direction. Our dataset comprises 144,000 frames acquired with a 128-channel Ouster LiDAR, synchronized RTK-GNSS trajectories, and MEMS-IMU measurements across 21 urban loops. To assess geometric consistency, we evaluated our method using alignment metrics based on road centerlines and intersections to capture both global and local accuracy. The proposed framework reduces the average global alignment error from 3.32m to 1.24m, achieving a 61.4% improvement, and significantly decreases the intersection centroid offset from 13.22m to 2.01m, corresponding to an 84.8% enhancement. The constructed high-fidelity map and raw dataset are publicly available through https://ieee-dataport.org/documents/perth-cbd-high-resolution-lidar-map-gnss-and-imu-calibration, and its visualization can be viewed at https://www.youtube.com/watch?v=-ZUgs1KyMks. The source code is available at https://github.com/HaitianWang/LiDAR-GNSS-and-IMU-Sensor-Fine-Alignment-through-Dynamic-Time-Warping-to-Construct-3D-City-Maps. This dataset and method together establish a new benchmark for evaluating 3D city mapping in GNSS-constrained environments.

翻译：基于LiDAR的三维建图存在累积漂移问题，导致全局失准，在GNSS受限环境中尤为明显。为解决此问题，我们提出一个融合LiDAR、GNSS与IMU数据的统一框架，用于高分辨率城市尺度建图。该方法采用动态时间规整进行基于速度的时间对准，并通过扩展卡尔曼滤波优化GNSS与IMU信号。局部地图采用基于正态分布变换的配准与闭环检测的位姿图优化构建，而全局一致性则通过GNSS约束锚点及重叠片段的精细配准来保证。我们还发布了在西澳大利亚珀斯采集的大规模多模态数据集，以推动该方向的未来研究。该数据集包含128通道Ouster LiDAR采集的144,000帧数据、同步的RTK-GNSS轨迹以及跨越21个城市环路的MEMS-IMU测量值。为评估几何一致性，我们采用基于道路中心线与交叉口的对准指标来评估方法的全局与局部精度。所提框架将平均全局对准误差从3.32米降低至1.24米，提升幅度达61.4%，并将交叉口质心偏移从13.22米显著减少至2.01米，对应84.8%的改进。构建的高保真地图与原始数据集已通过https://ieee-dataport.org/documents/perth-cbd-high-resolution-lidar-map-gnss-and-imu-calibration公开，可视化结果可访问https://www.youtube.com/watch?v=-ZUgs1KyMks查看。源代码发布于https://github.com/HaitianWang/LiDAR-GNSS-and-IMU-Sensor-Fine-Alignment-through-Dynamic-Time-Warping-to-Construct-3D-City-Maps。该数据集与方法共同为评估GNSS受限环境下的三维城市建图确立了新基准。