MosaicIMU: Carrier-Conditioned MoE Neural Inertial Odometry
What happened
Junye Zou and other authors released a preprint paper proposing MosaicIMU, a carrier-conditioned mixture-of-experts (MoE) pretraining and adaptation framework for generalizable neural inertial odometry. The framework combines carrier-specific expert features through a prototype-based router, decodes local velocity and uncertainty constraints, and integrates with a history-aware EKF. The authors state that when facing unseen domains, MosaicIMU freezes the pretrained base and learns lightweight expert residual branches; for edge deployment, it reuses the router to select online samples for incremental updates. The paper has been accepted as a Spotlight paper at CoRL 2026.
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- arXiv cs.ROMosaicIMU: A Generalizable Neural Inertial Odometry Framework for Multiple Carriers
MosaicIMU is a carrier-conditioned Mixture-of-Experts (MoE) pretraining and adaptation framework for generalizable neural inertial odometry. It composes carrier-specific expert features through a prototype-based router, decodes local velocity and uncertainty constraints, and integrates them with a history-aware EKF. For unseen domains, it freezes the pretrained base model and learns a lightweight expert residual branch; for edge deployment, it reuses the router to select online samples for incremental updates.
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