GLIO2: A GPU-Parallelized Tightly-Coupled LiDAR-Inertial-GNSS System
Overview
GLIO2 is a GPU-parallelized system for real-time global localization and mapping, achieving high accuracy in challenging environments like high-speed bridge traversal at 25 Hz on Jetson Orin NX. It uses a sliding-window factor graph for joint optimization of LiDAR, IMU, and GNSS data. (2026-10-09).
Generated from attributed reports · Updated 2 hours ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Reported quantity · horizontal accuracy: 1.6 other · Basis not reported
“) 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.”
Exact source · revision 1Source owner not reported
Reported quantity · trajectory length: 4.51 other · Basis not reported · Differing source assertions
“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”
Exact source · revision 1Source owner not reported
Reported quantity · processing time: 24 other · Basis not reported · Differing source assertions
“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”
Exact source · revision 1Source owner not reported
Reported quantity · processing time: 39.6 other · Basis not reported · Differing source assertions
“) 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.”
Exact source · revision 1Source owner not reported
Reported quantity · trajectory length: 5.66 other · Basis not reported · Differing source assertions
“) 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.”
Exact source · revision 1Source owner not reported
Reported quantity · processing speed: 25 other · Basis not reported
“) 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.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsGLIO2: A GPU-Parallelized Tightly-Coupled LiDAR-Inertial-GNSS System for Robust and Real-Time Global Localization and Mapping
This paper presents GLIO2, a GPU-parallelized tightly-coupled LiDAR-Inertial-GNSS system designed for robust and real-time global localization and mapping. It addresses limitations in existing fusion methods by jointly optimizing scan-to-multiscan LiDAR, IMU pre-integration, and raw GNSS measurements in a single sliding-window factor graph. The system achieves high accuracy in challenging environments, including a 5.66-km bridge traversed at up to 96 km/h, and runs at about 25 Hz on an NVIDIA Jetson Orin NX.
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