{"ID":2869378,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.14999","arxiv_id":"2509.14999","title":"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.","short_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 stat...","url_abs":"https://arxiv.org/abs/2509.14999","url_pdf":"https://arxiv.org/pdf/2509.14999v1","authors":"[\"Haoxuan Jiang\",\"Peicong Qian\",\"Yusen Xie\",\"Linwei Zheng\",\"Xiaocong Li\",\"Ming Liu\",\"Jun Ma\"]","published":"2025-09-18T14:33:36Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false}