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

2025-07-11

AI Summary

LiDAR-based 3D mapping suffers from cumulative drift in GNSS-denied or degraded environments, leading to global misalignment. To address this, we propose a tightly coupled multi-sensor fusion framework integrating LiDAR, GNSS, and IMU measurements. Our method employs dynamic time warping for velocity-level temporal synchronization, extended Kalman filtering for state estimation, GNSS anchor constraints, and fine-grained NTD (Normal Transform Distance) registration over overlapping segments. Additionally, pose-graph optimization and loop closure detection are integrated to jointly enhance local accuracy and global consistency. Evaluated on a large-scale multimodal urban dataset, the framework reduces global registration error from 3.32 m to 1.24 m (a 61.4% improvement). It significantly improves mapping robustness and accuracy under GNSS-limited conditions, enabling high-fidelity smart city modeling and GPS-denied navigation.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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. Our method reduces the average global alignment error from 3.32,m to 1.24,m, achieving a 61.4% improvement. The constructed high-fidelity map supports a wide range of applications, including smart city planning, geospatial data integration, infrastructure monitoring, and GPS-free navigation. Our method, and dataset together establish a new benchmark for evaluating 3D city mapping in GNSS-constrained environments. The dataset and code will be released publicly.

Problem

• Align LiDAR, GNSS, and IMU data to reduce mapping drift
• Improve global consistency in 3D city-scale mapping
• Address GNSS constraints for accurate urban environment modeling

Innovation

• Dynamic Time Warping for sensor alignment
• Extended Kalman filtering for signal refinement
• GNSS-constrained anchors for global consistency