  Inertial tracking is vital for robotic IoT and has gained popularity thanks
to 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 enhance the
tracking accuracy for indoor robotic applications, we introduce NeurIT, a
sequence-to-sequence framework that elevates tracking accuracy to a new level.
NeurIT employs a Time-Frequency Block-recurrent Transformer (TF-BRT) at its
core, combining the power of recurrent neural network (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
the magnetometer, which considerably reduces the tracking error. NeurIT is
implemented on a customized robotic platform and evaluated in various indoor
environments. Experimental results demonstrate that NeurIT achieves 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. NeurIT also
performs comparably to the visual-inertial approach (Tango Phone) in
vision-favored conditions and surpasses it in plain environments. We believe
NeurIT takes an important step forward toward practical neural inertial
tracking for ubiquitous and scalable tracking of robotic things. NeurIT,
including the source code and the dataset, is open-sourced here:
https://github.com/NeurIT-Project/NeurIT.
