Problem
Research questions and friction points this paper is trying to address.
LiDAR-Inertial-GNSS fusion
global localization
perceptually degraded environments
state estimation
scan-to-map registration
GLIO2: A GPU-Parallelized Tightly-Coupled LiDAR-Inertial-GNSS System for Robust and Real-Time Global Localization and Mapping
🤖 AI Summary
This study addresses the challenges of drift, error accumulation, and dynamic disturbances in LiDAR-fused localization within large-scale degenerate environments by proposing a GPU-accelerated, tightly coupled LiDAR-inertial-GNSS localization and mapping system. Methodologically, the system employs a sliding-window factor graph for joint multi-source optimization. A novel GPU-parallelized frontend is designed to simultaneously process point cloud scans, IMU pre-integration, and raw GNSS observations, which, combined with an offline backend batch refinement, effectively overcomes the divergence limitations inherent in conventional incremental mapping. Experimental results demonstrate that the proposed system maintains a horizontal accuracy of 1.6 meters in degenerate scenarios such as bridges and tunnels, achieves real-time operation at 25 Hz on edge computing hardware, and attains state-of-the-art accuracy across multiple benchmark datasets.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Tightly-Coupled Fusion
GPU Parallelization
Sliding-Window Factor Graph
Scan-to-Multiscan
Global Localization and Mapping
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