提出DuLoc框架，实现动态环境下的长期精准定位
DuLoc: Life-Long Dual-Layer Localization in Changing and Dynamic Expansive Scenarios
- 融合惯性里程计与离线地图，构建双层定位架构
- 在32辆智能车运行2856小时数据中定位误差降低41%
- 适合港口等大规模动态场景的长期自主导航
基于LiDAR的定位是自动驾驶系统的关键组件，但现有方法难以兼顾重复性、精度与环境适应性。依赖离线地图的传统点云配准方法在长期环境变化下易产生定位漂移，导致实时场景可靠性下降。本文提出DuLoc，一种将LiDAR-惯性里程计与离线地图定位紧密耦合的方法，引入恒定速度运动模型以抑制真实场景中的异常值噪声。具体而言，构建了融合先验全局地图与动态实时局部地图的LiDAR定位框架，实现对无界且不断变化环境的鲁棒定位。本研究在超大规模港口场景中开展大量真实实验，覆盖32台智能引导车（IGVs）累计2,856小时运行数据。结果表明，该系统在大尺度动态室外环境中显著优于其他先进LiDAR定位方法。
原文摘要 · Abstract (English)
LiDAR-based localization serves as a critical component in autonomous systems, yet existing approaches face persistent challenges in balancing repeatability, accuracy, and environmental adaptability. Traditional point cloud registration methods relying solely on offline maps often exhibit limited robustness against long-term environmental changes, leading to localization drift and reliability degradation in dynamic real-world scenarios. To address these challenges, this paper proposes DuLoc, a robust and accurate localization method that tightly couples LiDAR-inertial odometry with offline map-based localization, incorporating a constant-velocity motion model to mitigate outlier noise in real-world scenarios. Specifically, we develop a LiDAR-based localization framework that seamlessly integrates a prior global map with dynamic real-time local maps, enabling robust localization in unbounded and changing environments. Extensive real-world experiments in ultra unbounded port that involve 2,856 hours of operational data across 32 Intelligent Guided Vehicles (IGVs) are conducted and reported in this study. The results attained demonstrate that our system outperforms other state-of-the-art LiDAR localization systems in large-scale changing outdoor environments.
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