Inertial tracking is vital for robotic IoT and has gained popularity thanks to the ubiquity of low-cost inertial measurement units and deep learning-powered tracking algorithms. Existing works, however, have not fully utilized IMU measurements, particularly magnetometers, nor have they maximized the potential of deep learning to achieve the desired accuracy. To address these limitations, we introduceNeurIT, which elevates tracking accuracy to a new level.NeurITemploys a Time-Frequency Block-recurrent Transformer (TF-BRT) at its core, combining both RNN and Transformer to learn representative features in both time and frequency domains. To fully utilize IMU information, we strategically employ body-frame differentiation of magnetometers, considerably reducing the tracking error. We implementNeurITon a customized robotic platform and conduct evaluation in various indoor environments. Experimental results demonstrate thatNeurITachieves a mere 1-meter tracking error over a 300-meter distance. Notably, it significantly outperforms state-of-the-art baselines by 48.21% on unseen data. Moreover,NeurITdemonstrates robustness in large urban complexes and performs comparably to the visual-inertial approach (Tango Phone) in vision-favored conditions while surpassing it in feature-sparse settings. We believeNeurITtakes an important step forward toward practical neural inertial tracking for ubiquitous and scalable tracking of robotic things.NeurITis open-sourced here:https://github.com/aiot-lab/NeurIT.

Inertial tracking is vital for autonomous robots and has gained popularity with the ubiquity of low-cost Inertial Measurement Units (IMUs) and deep learning-powered tracking algorithms. Existing works, however, have not fully utilized IMU measurements, particularly magnetometers, nor maximized the potential of deep learning to achieve the desired accuracy. To bridge the gap, we introduce NeurIT, which employs a Time-Frequency Block-recurrent Transformer (TF-BRT) at its core, combining RNN and Transformer to learn both time-frequency representative features. To fully utilize IMU information, we strategically employ differentiation of body-frame magnetometers for orientation calibration in a sensor fusion manner. Experiments conducted in diverse environments show that NeurIT maintains a mere 1 -meter tracking error over a 300 - meter distance, surpassing state-of-the-art baselines by 48.21 % on unseen data. NeurIT also performs comparably to the visual-inertial approach (Tango Phone) in vision-favored conditions and surpasses it in plain environments. We share the code and data to promote further research: https://github.com/aiot-lab/NeurIT.