外观
论文arXiv 2609.203212 个月前
自主车辆GPS、IMU与LiDAR紧耦合SLAM融合的实现
原文链接：Implementation of Tightly-Coupled SLAM Fusion of GPS, IMU, and LiDAR for Autonomous Vehicles（arXiv 2609.20321 · 发布日期：2026-07-31）
Implementation of Tightly-Coupled SLAM Fusion of GPS, IMU, and LiDAR for Autonomous Vehicles
本文实现融合Velodyne 3D LiDAR、高频IMU与GPS的紧耦合SLAM，并在仿真和实体部署中验证连续定位建图。
核心要点
- 单一传感器存在失效点：LiDAR在无特征走廊退化，IMU漂移，GPS在城市峡谷易丢失。
- 方法先建立2D基线验证硬件同步与变换几何，再升级为FAST-LIO2驱动的3D架构。
- 3D系统用IESKF处理密集激光点和连续惯性数据，以消除高速运动模糊。
- GTSAM位姿图后端执行多模态回环；仿真和实体部署显示可生成高细节点云与HD地图基础。
验证范围与局限：验证仅描述为仿真环境和实体部署，摘要未给出具体平台、场景或任务。
英文摘要 (Abstract)
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.
作者：Amr O. Elmehrath, Farah Khaled, Rana Nahas, Catherine M. Elias
来源
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