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    "story_id": "mp-2026-10-09-011",
    "source_story_id": "tmp-story-glio2-joint-global-localization",
    "edition_id": "mp-2026-10-09-morning-0091",
    "edition_url": "https://themachinepress.com/edition/2026-10-09",
    "position": 11,
    "story_type": "dispatch",
    "section": "robotics",
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    "headline": "GNSS Got to Revisit the LiDAR Correspondences",
    "slug": "gnss-got-to-revisit-the-lidar-correspondences",
    "dek": "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.",
    "body_text": "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.",
    "limitations": [
      "Code and datasets were promised for release; the abstract does not establish that they were already available."
    ],
    "importance": 8,
    "canonical_url": "https://themachinepress.com/story/mp-2026-10-09-011/gnss-got-to-revisit-the-lidar-correspondences",
    "json_url": "https://themachinepress.com/story/mp-2026-10-09-011.json",
    "first_published_at": "2026-10-09T09:00:00.000-04:00",
    "modified_at": "2026-10-10T03:43:44.000-04:00",
    "content_status": "new",
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        "text": "GLIO2 jointly optimizes raw satellite, inertial and scan evidence instead of accepting one overconfident scan-to-map pose.",
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        "qualification": "Code and datasets were promised for release; the abstract does not establish that they were already available."
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    "source_ids": [
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    "tags": [
      "LiDAR",
      "GNSS fusion",
      "global localization"
    ],
    "image_url": null,
    "corrections": []
  },
  "sources": [
    {
      "source_id": "source-2026-10-09-011",
      "title": "arXiv preprint 2610.12411, original v1",
      "publisher": "arXiv / submitting research authors",
      "url": "https://arxiv.org/abs/2610.12411v1",
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      "source_type": "primary_research",
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      "published_at": "2026-10-08T13:49:33.000-04:00",
      "accessed_at": "2026-10-10T03:27:42.309-04:00",
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  "publisher": {
    "name": "The Machine Press",
    "url": "https://themachinepress.com",
    "description": "A daily newspaper for the age of artificial intelligence."
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  "cite_this_report": {
    "title": "GNSS Got to Revisit the LiDAR Correspondences",
    "publisher": "The Machine Press",
    "published_at": "2026-10-09T09:00:00.000-04:00",
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