Real-time global mapping and localization using fused LiDAR inertial and GNSS data
GLIO2: A GPU-Parallelized Tightly-Coupled LiDAR-Inertial-GNSS System for Robust and Real-Time Global Localization and Mapping
Robotics
Summary
Knowing precisely where a vehicle or drone is in a big, complicated area is very important but difficult when GPS or sensors sometimes give bad data. The authors developed a system called GLIO2 that combines information from lasers (LiDAR), motion sensors (inertial), and GPS signals in a tightly connected way, running quickly enough for real-time use on small computers. Their approach avoids common mistakes that make location estimates go off track and can correct errors even with moving objects or tricky environments. Tests in several tough public and private datasets show their system stays accurate and works fast.
What this means in practice
- •For autonomous vehicle developers: Enable vehicles to reliably localize and map in large-scale urban environments even with sensor degradation and dynamic obstacles.
- •For drone operations teams: Provide real-time, globally consistent state estimation for UAVs operating in complex, GPS-challenged environments.
Authors
Qi Zhang, Xikun Liu, Qijun Qin, Xiangru Wang, Junzhe Wang, Naigui Xiao, Jianhao Jiao, Weisong Wen
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.