MosaicIMU: Composing Carrier Experts for Generalizable Neural Inertial Odometry
Organizations: Department of Precision Instrument, Tsinghua University, Beijing 100084, China · School of Future Technology, Xi’an Jiaotong University, Xi’an 710049, China · College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China · School of Instrument Science and Opto-electronic Engineering, Beijing Information Science and Technology University, Beijing 100192, China · Key Laboratory of Complex System Intelligent Control and Decision Making, Beijing Institute of Technology, No. 5, South Zhongguancun Street, Haidian District, Beijing 100081, China · State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Haidian District, Beijing 100084, China
Abstract
Robust inertial odometry is essential for various carriers when external sensing is unreliable. Learning-based methods reduce integration drift by capturing local motion priors, but these methods often remain tied to a particular carrier, limiting generalization across heterogeneous platforms. We present MosaicIMU, a carrier-conditioned Mixture-of-Experts (MoE) pretraining-and-adaptation framework for generalizable neural inertial odometry. MosaicIMU uses a prototype-based router to compose carrier-specific expert features, decodes local velocity and uncertainty constraints, and integrates them with a history-aware EKF. For unseen domain adaptation, it freezes the pretrained base model and learns a new lightweight expert residual branch. For edge-deployment, it further reuses the router to select informative online samples for efficient incremental updates. Experiments show that MosaicIMU consistently outperforms learning-based baselines, reducing average ATE and RTE-10s by 40% and 34%, respectively. These results highlight that MosaicIMU provides a scalable pretraining-to-deployment paradigm for generalizable and adaptive neural inertial odometry.