GLIO2：一种用于鲁棒实时全局定位与建图的GPU并行紧耦合激光雷达-惯性-全球导航卫星系统（GNSS）系统
GLIO2: A GPU-Parallelized Tightly-Coupled LiDAR-Inertial-GNSS System for Robust and Real-Time Global Localization and Mapping
- The Hong Kong Polytechnic University（香港理工大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出GLIO2，一种GPU并行紧耦合激光雷达-惯性-GNSS系统，通过滑动窗口因子图联合优化多传感器数据，在多基准测试中实现最优全局定位精度，边缘硬件上可实时运行，桥梁场景下对比基线发散时仍保持1.6米水平精度。
AI中文摘要：
在感知退化的大规模环境中实现全局一致的实时状态估计，对自动驾驶车辆和空中机器人至关重要，该过程需要融合激光雷达、惯性测量单元（IMU）和GNSS的测量数据。然而，现有的融合方法采用的是扫描到地图前端，存在两种失效模式：其一，每次扫描与增量构建的地图对齐，当地图处于退化状态时会发生漂移，一旦估计结果发散，误差便无法恢复；其二，即便未发生发散，由动态物体或错误对应关系导致的配准偏差会作为单一位姿约束传播，且伴随置信度过高的协方差，使得GNSS无法对其对应关系进行重新加权或重新线性化。本文提出GLIO2，这是一种紧耦合激光雷达-惯性-GNSS系统，其GPU并行前端在单个滑动窗口因子图中联合优化扫描到多扫描激光雷达、IMU预积分以及原始GNSS测量数据，可在边缘硬件上维持实时运行；互补的离线后端会复用相同的缓存因子，以批量方式优化整个轨迹，在约24秒内完成30分钟、4.51公里的UrbanNav黄埔序列。在三个公开基准（UrbanNav、MARS-LVIG、M3DGR）以及自行采集的无人机和车辆数据上，GLIO2在所有评估系统中实现了最佳整体精度；在一段5.66公里、最高行驶速度达96公里/小时的桥梁路段上，所有对比基线均因激光雷达退化而发散，而GLIO2保持了1.6米的水平精度；在NVIDIA Jetson Orin NX上，完整 pipeline 以约25赫兹（每次扫描耗时39.60毫秒）运行，源代码和数据集将公开。
英文摘要：
Globally consistent, real-time state estimation in large-scale, perceptually degraded environments is essential for autonomous vehicles and aerial robots, and requires fusing LiDAR, inertial, and GNSS measurements. Existing fusion methods, however, share a scan-to-map front-end with two failure modes. First, each scan is aligned to an incrementally built map that drifts under degeneracy, and once the estimate diverges the error is irrecoverable. Second, even without divergence, a registration biased by dynamic objects or wrong correspondences is propagated as a single pose constraint with an over-confident covariance, leaving its correspondences unavailable for GNSS to re-weight or relinearize. We propose GLIO2, a tightly-coupled LiDAR-Inertial-GNSS system whose GPU-parallel front-end jointly optimizes scan-to-multiscan LiDAR, IMU pre-integration, and raw GNSS measurements in a single sliding-window factor graph, sustaining real-time operation on edge hardware. A complementary offline back-end reuses the same cached factors to refine the entire trajectory in batch, completing the 30-min, 4.51-km UrbanNav Whampoa sequence in about 24 s. Across three public benchmarks (UrbanNav, MARS-LVIG, M3DGR) and self-collected UAV and vehicle data, GLIO2 attains the best overall accuracy among evaluated systems. On a 5.66-km bridge traversed at up to 96 km/h, where every competing baseline diverges under LiDAR degeneracy, it maintains 1.6 m horizontal accuracy. On an NVIDIA Jetson Orin NX, the full pipeline runs at about 25 Hz (39.60 ms per scan). The source code and datasets will be released.
