Semantic-LiDAR-Inertial-Wheel Odometry Fusion for Robust Localization in Large-Scale Dynamic Environments
Haoxuan Jiang, Peicong Qian, Yusen Xie, Linwei Zheng, Xiaocong Li, Ming Liu, Jun Ma
AI summary
Problem
Reliable global localization in large-scale dynamic environments like automated ports is hindered by GPS signal loss, LiDAR drift from moving objects, and inaccurate wheel odometry on complex terrains.
Approach
The system tightly fuses LiDAR, IMU, and wheel odometry data using an iterative error-state Kalman filter, enhanced by a semantic voxel map to filter dynamic features and a 3D adaptive scaling strategy to dynamically adjust wheel odometry weights based on terrain conditions.
Key results
- iESKF-based multi-sensor fusion framework mitigates motion distortion and localization drift
- Semantic voxel matching algorithm distinguishes static from dynamic objects to reduce long-term trajectory drift
- 3D adaptive scaling strategy dynamically adjusts wheel odometry weights for complex terrains
- Validated on 3,575 hours of data from 35 IGVs in a 1M sq meter automated port, outperforming state-of-the-art LiDAR methods
Why it matters
Provides a reliable, drift-free localization solution for autonomous industrial vehicles operating in large-scale dynamic environments where GPS is unavailable and traditional SLAM fails.
Abstract
Reliable, drift-free global localization presents significant challenges yet remains crucial for autonomous nav- igation 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 trajec- tory 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 en- sures 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.