robotics
The Inertial Model Survived a Sensor Remount
A rotation-equivariant interface cut unseen-remount error without retraining.
Summary
A rotation-equivariant interface cut unseen-remount error without retraining.
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.
Limits and context
- Results span selected datasets and platforms, not every sensor or motion regime.
Key claims
A rotation-equivariant interface cut unseen-remount error without retraining.
Qualification: Results span selected datasets and platforms, not every sensor or motion regime.
Evidence: source-2026-09-23-008
Sources
- arXiv preprint 2609.25338arXiv · primary research
Corrections
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