Plug-and-Play Sparse Inertial Motion Tracking With Sim-to-Real Transfer

Bachhuber S, Lehmann D, Dorschky E, Koelewijn A, Seel T, Weygers I (2023)

Publication Type: Journal article

Publication year: 2023


Book Volume: 7

Article Number: 7004904

Journal Issue: 10

DOI: 10.1109/LSENS.2023.3307122


Inertial measurement units (IMUs) are used for inertial motion tracking (IMT) in a growing number of applications as sensor fusion methods are being advanced in three directions: magnetometer-free IMT methods that eliminate the effect of magnetic disturbances; sparse IMT approaches that lead to reduced setup complexity; and automatic self-calibration of sensor-to-segment positions or orientations. In this letter, we propose an approach that combines all three achievements and, for the first time, enables plug-and-play, magnetometer-free, and sparse IMT. This is accomplished by training a recurrent neural-network-based observer (RNNo) on just-in-time generated simulated motion data of kinematic chains. We demonstrate that domain-specific training data augmentations lead to a trained RNNo which zero shot generalizes to previously unseen experimental data and, thus, overcomes the sim-to-real gap. The trained RNNo achieves a tracking error of < 4 degrees when estimating the relative pose of a three-segment kinematic chain with two hinge joints. The proposed method offers a novel simulation-data-driven approach for solving complex sparse sensing problems while assuring robust and plug-and-play generalizability to experimental data.

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Bachhuber, S., Lehmann, D., Dorschky, E., Koelewijn, A., Seel, T., & Weygers, I. (2023). Plug-and-Play Sparse Inertial Motion Tracking With Sim-to-Real Transfer. IEEE Sensors Letters, 7(10).


Bachhuber, Simon, et al. "Plug-and-Play Sparse Inertial Motion Tracking With Sim-to-Real Transfer." IEEE Sensors Letters 7.10 (2023).

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