RNN-based Observability Analysis for Magnetometer-Free Sparse Inertial Motion Tracking

Bachhuber S, Weber D, Weygers I, Seel T (2022)

Publication Type: Conference contribution

Publication year: 2022

Publisher: Institute of Electrical and Electronics Engineers Inc.

Conference Proceedings Title: 2022 25th International Conference on Information Fusion, FUSION 2022

Event location: Linkoping SE

ISBN: 9781737749721

DOI: 10.23919/FUSION49751.2022.9841375

Abstract

Inertial measurement units are widely used for motion tracking of kinematic chains in numerous applications. While magnetometer-free sensor fusion enables reliably high accuracy in indoor environments and near magnetic disturbances, the use of sparse sensor setups would yield additional advantages in cost, effort, and usability. However, it is unclear which sparse sensor setups can be used to track which motions of which kinematic chains, since observability of the underlying nonlinear dynamics is barely understood to date. We propose a method that utilizes recurrent neural networks (RNNs) and automatically generated training data to assess the observability of the relative pose of kinematic chains in sparse inertial motion tracking (IMT) systems. We apply this method to a range of double-hinge-joint systems that perform fully-exciting random motion. Results show how the degree of observability depends on the kinematic structure and that RNN-based observers can achieve small tracking errors in a large range of sparse and magnetometer-free setups. The proposed methods enable systematic assessment of observability properties in complex nonlinear dynamics and represent a key step toward enabling reliably accurate and non-restrictive IMT solutions.

Authors with CRIS profile

Simon Bachhuber Professur für Intelligente Sensomotorische Systeme Ive Weygers Professur für Intelligente Sensomotorische Systeme Thomas Seel Professur für Intelligente Sensomotorische Systeme

Involved external institutions

Technische Universität Berlin DE Germany (DE)

How to cite

APA:

Bachhuber, S., Weber, D., Weygers, I., & Seel, T. (2022). RNN-based Observability Analysis for Magnetometer-Free Sparse Inertial Motion Tracking. In 2022 25th International Conference on Information Fusion, FUSION 2022. Linkoping, SE: Institute of Electrical and Electronics Engineers Inc..

MLA:

Bachhuber, Simon, et al. "RNN-based Observability Analysis for Magnetometer-Free Sparse Inertial Motion Tracking." Proceedings of the 25th International Conference on Information Fusion, FUSION 2022, Linkoping Institute of Electrical and Electronics Engineers Inc., 2022.

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