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    "headline": "The Inertial Model Survived a Sensor Remount",
    "slug": "the-inertial-model-survived-a-sensor-remount",
    "dek": "A rotation-equivariant interface cut unseen-remount error without retraining.",
    "summary": "A rotation-equivariant interface cut unseen-remount error without retraining.",
    "body_text": "GINIO constrains neural inertial-odometry predictions so motion vectors and uncertainty transform consistently when an IMU is mounted at a new orientation. On the Fetch benchmark, the authors report that unseen physical-remount absolute trajectory error fell from 8.15 meters to 0.50 meters without retraining. Other tested backbones also improved while using fewer operations than one comparison. Results span selected datasets and platforms, not every sensor or motion regime.",
    "why_it_matters": "A rotation-equivariant interface cut unseen-remount error without retraining.",
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      "Results span selected datasets and platforms, not every sensor or motion regime."
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        "qualification": "Results span selected datasets and platforms, not every sensor or motion regime."
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    "tags": [
      "inertial odometry",
      "equivariance",
      "robot navigation"
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      "source_id": "source-2026-09-23-008",
      "title": "arXiv preprint 2609.25338",
      "publisher": "arXiv",
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    "title": "The Inertial Model Survived a Sensor Remount",
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