GINIO: A Geometric SO(3)-Equivariant Interface for Neural Inertial Odometry
Neural inertial odometry increasingly uses networks as learned measurements inside filtering pipelines. Such measurements should transform consistently under arbitrary IMU mounting conventions: their mean must transform as a vector, and their covariance must transform congruently as a second-order tensor. We present GINIO, a geometric SO(3)-equivariant interface for neural inertial odometry under arbitrary rotations of the IMU measurement frame. Given calibrated IMU windows, our framework predicts a motion measurement and uncertainty obeying these tensorial laws. To support efficient sensor-frame learning, we introduce Last-Frame Alignment (LFA), a deterministic preprocessing step that is...
| Authors | Chankyo Kim, Minghan Zhu, Tzu-Yuan Lin et al. |
|---|---|
| Primary category | cs.RO |
| Published | 21 September 2026 |
| Last updated at source | 27 September 2026 |
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