الملخص
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.
الكلمات المفتاحية
الموضوع
بيانات النشر
- المجلة
- غير متاح
- وصول مفتوح
- وصول مفتوح أخضر
اقتبس هذه المقالة
APA 7
Zhang, Q., Liu, X., Qin, Q., Wang, X., Wang, J., Xiao, N., Jiao, J., & Wen, W. (2026). GLIO2: A GPU-Parallelized Tightly-Coupled LiDAR-Inertial-GNSS System for Robust and Real-Time Global Localization and Mapping. https://omanscience.com/ar/articles/glio2-a-gpu-parallelized-tightly-coupled-lidar-inertial-gnss-system-for-robust-and-real-time-global-localization-and-mapping
MLA 9
Zhang, Qi, et al. "GLIO2: A GPU-Parallelized Tightly-Coupled LiDAR-Inertial-GNSS System for Robust and Real-Time Global Localization and Mapping." https://omanscience.com/ar/articles/glio2-a-gpu-parallelized-tightly-coupled-lidar-inertial-gnss-system-for-robust-and-real-time-global-localization-and-mapping.
شيكاغو (المؤلف–التاريخ)
Zhang, Qi, Xikun Liu, Qijun Qin, Xiangru Wang, Junzhe Wang, Naigui Xiao, Jianhao Jiao, and Weisong Wen. 2026. "GLIO2: A GPU-Parallelized Tightly-Coupled LiDAR-Inertial-GNSS System for Robust and Real-Time Global Localization and Mapping." https://omanscience.com/ar/articles/glio2-a-gpu-parallelized-tightly-coupled-lidar-inertial-gnss-system-for-robust-and-real-time-global-localization-and-mapping.
هارفارد
Zhang, Q., Liu, X., Qin, Q., Wang, X., Wang, J., Xiao, N., Jiao, J. and Wen, W. (2026) 'GLIO2: A GPU-Parallelized Tightly-Coupled LiDAR-Inertial-GNSS System for Robust and Real-Time Global Localization and Mapping', Available at: https://omanscience.com/ar/articles/glio2-a-gpu-parallelized-tightly-coupled-lidar-inertial-gnss-system-for-robust-and-real-time-global-localization-and-mapping.
فانكوفر
Zhang Q, Liu X, Qin Q, Wang X, Wang J, Xiao N, et al. GLIO2: A GPU-Parallelized Tightly-Coupled LiDAR-Inertial-GNSS System for Robust and Real-Time Global Localization and Mapping. https://omanscience.com/ar/articles/glio2-a-gpu-parallelized-tightly-coupled-lidar-inertial-gnss-system-for-robust-and-real-time-global-localization-and-mapping
IEEE
Q. Zhang, X. Liu, Q. Qin, X. Wang, J. Wang, N. Xiao, J. Jiao, and W. Wen, "GLIO2: A GPU-Parallelized Tightly-Coupled LiDAR-Inertial-GNSS System for Robust and Real-Time Global Localization and Mapping," https://omanscience.com/ar/articles/glio2-a-gpu-parallelized-tightly-coupled-lidar-inertial-gnss-system-for-robust-and-real-time-global-localization-and-mapping.