面向自动驾驶车辆的GPS、IMU与LiDAR紧耦合SLAM融合实现
Implementation of Tightly-Coupled SLAM Fusion of GPS, IMU, and LiDAR for Autonomous Vehicles
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- C-DRiVeS Lab: Cognitive Driving Research in Vehicular Systems（C-DRiVeS实验室：车辆系统认知驾驶研究实验室）
- German University in Cairo（开罗德国大学）
机构由 AI 辅助整理，请以论文原文为准。
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中文总结 AI 辅助
本文实现了一种紧耦合SLAM融合架构，集成GPS、IMU和LiDAR，利用IESKF和GTSAM后端消除传感器盲区与漂移，生成高清地图以支持自动驾驶规划。
中文摘要 AI 辅助
自动驾驶车辆完全依赖同时定位与建图（SLAM）在未知环境中安全导航。然而，依赖单一传感模态会引入关键失效点：LiDAR系统在无特征走廊中性能退化，惯性测量单元（IMU）累积数学漂移，GPS在城市峡谷中频繁丢失信号。本文提出了一种紧耦合SLAM融合架构的实现，该架构集成了Velodyne 3D LiDAR、高频IMU和GPS，以实现连续的空间感知。采用分阶段开发方法，我们建立了2D基线以验证硬件同步和变换几何，随后升级至由FAST-LIO2驱动的完整3D架构。这种先进方法使用迭代误差状态卡尔曼滤波器（IESKF）处理密集的3D激光点以及连续的惯性数据，消除了高速运动模糊。为消除长期漂移，GTSAM位姿图优化后端执行多模态闭环检测。在模拟环境和物理部署中的评估结果表明，紧耦合3D融合有效克服了单个传感器的盲区，生成了高细节点云，为高级下游自动驾驶规划器提供了所需的基础高清（HD）地图。
英文摘要
Autonomous vehicles depend entirely on Simultaneous Localization and Mapping (SLAM) to navigate safely in unknown environments. However, relying on a single sensory modality introduces critical failure points: LiDAR systems degrade in featureless corridors, Inertial Measurement Units (IMUs) accumulate mathematical drift, and GPS drops frequently in urban canyons. This paper presents the implementation of a tightly-coupled SLAM fusion architecture that integrates a Velodyne 3D LiDAR, a high-frequency IMU, and GPS to achieve continuous spatial awareness. Utilizing a phased development methodology, we establish a 2D baseline to validate hardware synchronization and transform geometries before upgrading to a full 3D architecture driven by FAST-LIO2. This advanced approach uses an Iterated Error-State Kalman Filter (IESKF) to process dense 3D laser points alongside continuous inertial data, eliminating motion blur at high speeds. To eradicate long-term drift, a GTSAM pose-graph optimization back-end executes multi-modal loop closures. Evaluated across simulated environments and physical deployments, the results demonstrate that tightly-coupled 3D fusion effectively overcomes individual sensor blind spots to generate highly detailed point clouds, providing the foundational High-Definition (HD) maps required for advanced downstream autonomous planners.
