Semantic-LiDAR-Inertial-Wheel Odometry Fusion for Robust Localization in Large-Scale Dynamic Environments
Abstract
Reliable, drift-free global localization presents significant challenges yet remains crucial for autonomous navigation in large-scale dynamic environments. In this paper, we introduce a tightly-coupled Semantic-LiDAR-Inertial-Wheel Odometry fusion framework, which is specifically designed to provide high-precision state estimation and robust localization in large-scale dynamic environments. Our framework leverages an efficient semantic-voxel map representation and employs an improved scan matching algorithm, which utilizes global semantic information to significantly reduce long-term trajectory drift. Furthermore, it seamlessly fuses data from LiDAR, IMU, and wheel odometry using a tightly-coupled multi-sensor fusion Iterative Error-State Kalman Filter (iESKF). This ensures reliable localization without experiencing abnormal drift. Moreover, to tackle the challenges posed by terrain variations and dynamic movements, we introduce a 3D adaptive scaling strategy that allows for flexible adjustments to wheel odometry measurement weights, thereby enhancing localization precision. This study presents extensive real-world experiments conducted in a one-million-square-meter automated port, encompassing 3,575 hours of operational data from 35 Intelligent Guided Vehicles (IGVs). The results consistently demonstrate that our system outperforms state-of-the-art LiDAR-based localization methods in large-scale dynamic environments, highlighting the framework's reliability and practical value.
Keywords
Cite
@article{arxiv.2509.14999,
title = {Semantic-LiDAR-Inertial-Wheel Odometry Fusion for Robust Localization in Large-Scale Dynamic Environments},
author = {Haoxuan Jiang and Peicong Qian and Yusen Xie and Linwei Zheng and Xiaocong Li and Ming Liu and Jun Ma},
journal= {arXiv preprint arXiv:2509.14999},
year = {2025}
}