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

TL;DR

LiDAR-based city-scale mapping suffers from cumulative drift in GNSS-denied or obstructed environments. The paper presents LIGMA, a unified LiDAR-GNSS-IMU fusion framework that uses velocity-based DTW for cross-sensor synchronization, EKF refinement of GNSS/IMU data, and hierarchical mapping via NDT, pose-graph optimization with loop closure, and ICP refinement to achieve global consistency. A large Perth CBD dataset with 144,000 LiDAR frames, RTK-GNSS trajectories, and MEMS-IMU data across 21 loops is introduced to benchmark GNSS-constrained urban mapping. Quantitative results show the global centreline RMSE decreases from $3.32$ m to $1.24$ m and the mean intersection centroidal offset drops from $13.22$ m to $2.01$ m, highlighting substantial improvements in both global and local alignment and establishing a new benchmark for GNSS-constrained 3D city mapping. The work enables high-fidelity HD maps for autonomous navigation, urban planning, and change detection, with future directions including GNSS-denied operation and real-time processing.

Abstract

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 is publicly available through https://ieee-dataport.org/documents/perth-cbd-high-resolution-lidar-map-gnss-and-imu-calibration and its visualization can be viewed in the provided in https://www.youtube.com/watch?v=-ZUgs1KyMks. This dataset and method together establish a new benchmark for evaluating 3D city mapping in GNSS-constrained environments.

Figures (14)

• Figure 1: Overview of the proposed framework for 3D city mapping which comprises: (1) multimodal data capture via Ouster OS1-128 LiDAR and RUG-3 GNSS/IMU; (2) preprocessing with velocity-based alignment and Dynamic Time Warping synchronization; (3) GNSS-calibrated 3D mapping using NDT, pose graph optimization, and global ICP; and (4) accuracy evaluation via skeleton-based centreline metrics against 2D cartographic references.
• Figure 2: Survey layout of the Perth CBD dataset with 21 manually designed loops (Seq 1–21) spanning 18.6km of urban roads, providing diverse viewpoints, repeated coverage, and intersection overlap for robust SLAM and high-quality 3D mapping.
• Figure 3: Sensor setup for vehicle-based data collection in central Perth, featuring a roof-mounted 128-channel LiDAR (red), dual GNSS antennas (purple and blue), and a high-grade inertial unit (green), all precisely aligned. (a) Schematic with dimensions, (b) real-world deployment, and (c--d) core sensor modules.
• Figure 4: Point cloud distribution and density - Left: Point count histogram vs. distance from LiDAR origin; Middle: Cumulative distribution showing 75% of points within 22.4m; Right: Top-down heatmap with log-scaled point density, highlighting near-field concentration.
• Figure 5: Overview of the proposed LIGMA pipeline for urban LiDAR–GNSS 3D map construction. The method comprises four stages: (1) LiDAR and GNSS/IMU preprocessing via denoising, downsampling, and temporal interpolation; (2) soft synchronization using velocity estimation, feature matching, and Dynamic Time Warping; (3) multimodal map construction through NDT registration, GNSS-constrained correction, and ICP-based merging; and (4) quantitative evaluation using centerline and intersection-level alignment metrics.