论文/arXiv 2610.12411·20 天前

GLIO2：一种用于鲁棒实时全局定位与建图的 GPU 并行化紧耦合 LiDAR-惯性-GNSS 系统

GLIO2通过GPU并行滑窗因子图紧耦合LiDAR、IMU与GNSS，在公开基准和自采数据上验证实时全局定位建图。

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原文链接：GLIO2: A GPU-Parallelized Tightly-Coupled LiDAR-Inertial-GNSS System for Robust and Real-Time Global Localization and Mapping（arXiv 2610.12411 · 发布日期：2026-10-08）

GLIO2: A GPU-Parallelized Tightly-Coupled LiDAR-Inertial-GNSS System for Robust and Real-Time Global Localization and Mapping

GLIO2通过GPU并行滑窗因子图紧耦合LiDAR、IMU与GNSS，在公开基准和自采数据上验证实时全局定位建图。

核心要点

• 现有扫描到地图前端在退化环境易漂移发散，并会传播动态物体或误匹配偏差。
• 前端在单一滑窗因子图中联合优化多扫描LiDAR、IMU预积分和原始GNSS测量。
• 在UrbanNav、MARS-LVIG、M3DGR及自采UAV和车辆数据上总体精度最佳。
• 离线后端复用缓存因子批量优化全轨迹；源码和数据集将发布。

验证范围与局限：验证覆盖三项公开基准、自采UAV和车辆数据，以及5.66 km桥梁场景与NVIDIA Jetson Orin NX边缘硬件。

英文摘要 (Abstract)

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.

作者：Qi Zhang, Xikun Liu, Qijun Qin, XiangRu Wang, Junzhe Wang, Naigui Xiao, Jianhao Jiao, Weisong Wen

来源

本页事实对应的公开页面，以原文为准。

1. arXiv 2610.12411：GLIO2: A GPU-Parallelized Tightly-Coupled LiDAR-Inertial-GNSS System for Robust and Real-Time Global Localization and Mapping访问 2026-10-21