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.

翻译：稳健的惯性里程计对于外部传感不可靠环境中的各类载体至关重要。基于学习的方法通过捕获局部运动先验来减少积分漂移，但这些方法通常受限于特定载体，难以在异构平台间泛化。我们提出MosaicIMU，一种基于载体条件混合专家（MoE）的预训练与适配框架，用于通用化神经惯性里程计。MosaicIMU利用原型路由器组合载体特定专家特征，解码局部速度与不确定性约束，并通过历史感知的扩展卡尔曼滤波器（EKF）进行融合。针对未见域适配，该方法冻结预训练基模型并学习轻量级专家残差分支；针对边缘部署，进一步复用路由器来选择信息丰富的在线样本以进行高效增量更新。实验表明，MosaicIMU持续优于基于学习的基线方法，将平均绝对轨迹误差（ATE）和10秒相对轨迹误差（RTE-10s）分别降低40%和34%。这些结果凸显MosaicIMU为通用化与自适应的神经惯性里程计提供了可扩展的预训练到部署范式。