Inertial odometry (IO) using only Inertial Measurement Units (IMUs) provides a lightweight solution for human motion tracking in augmented reality (AR) and wearable devices. Recent learning-based IO methods have improved the generalizability of inertial localization through large-scale pretraining on human motion datasets. However, these approaches remain prone to drift and noise because they do not explicitly capture human motion dynamics, especially on daily activity datasets such as Nymeria. In this work, we propose to ground inertial odometry in human kinematics through a learned IMU-inferred pose prior, which promotes physically consistent motion constraints. We integrate this pose prior into existing IO architectures and reduce positional drift by up to 36% on the challenging Nymeria dataset, which is 5x larger than datasets used in prior work. We further improve long-term performance with a sensor-fusion framework that incorporates auxiliary signals from lightweight sensors already available on commercial AR glasses, including magnetometers, barometers, and secondary IMUs. With this fusion strategy, positional drift is reduced by up to 42%, improving robustness and generalization across diverse motion conditions. Together, our results introduce a new paradigm for inertial and lightweight odometry by unifying human motion kinematics with multimodal sensing, setting a new benchmark for accurate and robust camera-less human tracking. Our website is available at https://spice-lab.org/projects/MARIO/.

翻译：仅使用惯性测量单元（IMU）的惯性里程计为增强现实（AR）及可穿戴设备中的人体运动追踪提供了轻量化解决方案。近年来基于学习的惯性里程计方法通过在大规模人体运动数据集上进行预训练，提升了惯性定位的泛化能力。然而，这些方法仍易受漂移和噪声影响，因为它们未能显式捕捉人体运动动力学特征，尤其是在针对日常活动数据集（如Nymeria）时。在本工作中，我们提出通过学习IMU推导的姿态先验，将惯性里程计嵌入人体运动学框架，从而促进物理一致的运动约束。我们将该姿态先验集成至现有惯性里程计架构中，在具有挑战性的Nymeria数据集上将位置漂移降低达36%，该数据集规模是先前工作中所用数据集的5倍。我们进一步通过传感器融合框架提升长期性能，该框架融合了商用AR眼镜中已有的轻量化传感器（包括磁力计、气压计和辅助IMU）所提供的辅助信号。借助此融合策略，位置漂移降低达42%，在不同运动条件下的鲁棒性和泛化能力得到增强。综合而言，我们的成果通过统一人体运动运动学与多模态传感，为惯性及轻量化里程计引入了新的范式，为准确鲁棒的无相机人体追踪设立了新基准。项目网站见https://spice-lab.org/projects/MARIO/。