[Submitted on 21 Sep 2026]
Title:GINIO: A Geometric SO(3)-Equivariant Interface for Neural Inertial Odometry
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Abstract: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 provably equivalent to world-frame training for SO(3)-equivariant predictors. The connected estimator tracks sensor-local states such as IMU bias, separating nuisance estimation from the geometric law enforced by the learned measurement. We instantiate the same interface in filter-connected NIO, AirIO-style recurrent aerial prediction, EqNIO-style full-SO(3) canonicalization, and ResNet-style temporal backbones. On TLIO, GINIO achieves 2.018 m ID/SO(3) ATE while EqNIO degrades to 76.389 m, using 11.6x fewer FLOPs. On NanoBench, our AirIO-style instantiation improves ATE from 5.579 m to 1.430 m without external attitude input, and our ResNet-style instantiation reaches 0.581 m ATE versus 0.645 m for ResNet1D. On Fetch, GINIO empirically reduces unseen physical-remount ATE from 8.15 m to 0.50 m without retraining, demonstrating robustness beyond the exact coordinate-frame guarantee. For uncertainty, spectral covariance reduces covariance-equivariance error by over three orders of magnitude compared with a diagonal head.
Comments: Accepted at the 10th Conference on Robot Learning (CoRL 2026). 26 pages, 14 figures
Subjects:
Robotics (cs.RO); Machine Learning (cs.LG)
Cite as: arXiv:2609.25338 [cs.RO]
(or arXiv:2609.25338v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.25338
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Chankyo Kim [view email] [v1] Mon, 21 Sep 2026 19:28:14 UTC (4,829 KB)
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