MosaicIMU: Composing Carrier Experts for Generalizable Neural Inertial Odometry
Overview
This paper presents MosaicIMU, a carrier-conditioned Mixture-of-Experts framework designed to improve neural inertial odometry across heterogeneous platforms. By using a prototype-based router to compose carrier-specific expert features and integrating them with a history-aware Extended Kalman Filter (EKF), the method reduces average Absolute Trajectory Error (ATE) and Relative Translation Error over 10 seconds (RTE-10s) by 40% and 34%, respectively, compared to learning-based baselines. The approach supports domain adaptation through freezing a pretrained base model while learning lightweight expert residual branches, and enables efficient edge deployment via online sample selection for incremental updates.
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- arXiv Robotics — research abstractsSignalMosaicIMU: Composing Carrier Experts for Generalizable Neural Inertial Odometry
This paper presents MosaicIMU, a carrier-conditioned Mixture-of-Experts framework designed to improve neural inertial odometry across heterogeneous platforms. By using a prototype-based router to compose carrier-specific expert features and integrating them with a history-aware Extended Kalman Filter (EKF), the method reduces average Absolute Trajectory Error (ATE) and Relative Translation Error over 10 seconds (RTE-10s) by 40% and 34%, respectively, compared to learning-based baselines. The approach supports domain adaptation through freezing a pretrained base model while learning lightweight expert residual branches, and enables efficient edge deployment via online sample selection for incremental updates.
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