Semantic-LiDAR-Inertial-Wheel Odometry Fusion for Robust Localization in Large-Scale Dynamic Environments
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911161832701952 |
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| author | Jiang, Haoxuan Qian, Peicong Xie, Yusen Zheng, Linwei Li, Xiaocong Liu, Ming Ma, Jun |
| author_facet | Jiang, Haoxuan Qian, Peicong Xie, Yusen Zheng, Linwei Li, Xiaocong Liu, Ming Ma, Jun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_14999 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Semantic-LiDAR-Inertial-Wheel Odometry Fusion for Robust Localization in Large-Scale Dynamic Environments Jiang, Haoxuan Qian, Peicong Xie, Yusen Zheng, Linwei Li, Xiaocong Liu, Ming Ma, Jun Robotics 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. |
| title | Semantic-LiDAR-Inertial-Wheel Odometry Fusion for Robust Localization in Large-Scale Dynamic Environments |
| topic | Robotics |
| url | https://arxiv.org/abs/2509.14999 |