robotics
GNSS Got to Revisit the LiDAR Correspondences
GLIO2 jointly optimizes raw satellite, inertial and scan evidence instead of accepting one overconfident scan-to-map pose.
Summary
GLIO2 jointly optimizes raw satellite, inertial and scan evidence instead of accepting one overconfident scan-to-map pose.
GLIO2 combines scan-to-multiscan LiDAR, inertial pre-integration and raw GNSS measurements in one sliding-window factor graph with a GPU-parallel front end. Keeping the underlying factors lets the system reconsider evidence that a single compressed pose could obscure. On a 5.66-kilometer bridge driven at up to 96 kilometers per hour, it maintained 1.6-meter horizontal accuracy while the evaluated competing baselines diverged. The full pipeline ran at about 25 hertz on a Jetson Orin NX, or 39.60 milliseconds per scan. An offline back end refined a separate thirty-minute, 4.51-kilometer sequence in about twenty-four seconds. Code and datasets were promised for release; the abstract does not establish that they were already available.
Why it matters
GLIO2 jointly optimizes raw satellite, inertial and scan evidence instead of accepting one overconfident scan-to-map pose.
Limits and context
- Code and datasets were promised for release; the abstract does not establish that they were already available.
Key claims
GLIO2 jointly optimizes raw satellite, inertial and scan evidence instead of accepting one overconfident scan-to-map pose.
Qualification: Code and datasets were promised for release; the abstract does not establish that they were already available.
Evidence: source-2026-10-09-011
Sources
- arXiv preprint 2610.12411, original v1arXiv / submitting research authors · primary research
Corrections
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