LiDAR, GNSS, and IMU Sensor Fine Alignment Through Dynamic Time Warping to Construct 3-D City Maps

(LiDAR)-based 3-D mapping suffers from cumulative drift causing global misalignment, particularly in global navigation satellite system (GNSS)-constrained environments. To address this, we propose a unified framework that fuses LiDAR, GNSS, and inertial measurement unit (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 3.32 to 1.24 m, achieving a 61.4% improvement, and significantly decreases the intersection centroid offset from 13.22 to 2.01 m, corresponding to an 84.8% enhancement. The constructed high-fidelity map and raw dataset are publicly available through IEEE Dataport and its visualization can be viewed in the provided Demo. This dataset and method together establish a new benchmark for evaluating 3D city mapping in GNSS-constrained environments, with source code available at GitHub Repository.