Neural networks versus conventional filters for inertial-sensor-based attitude estimation
Weber D, Guhmann C, Seel T (2020)
Publication Type: Conference contribution
Publication year: 2020
Publisher: Institute of Electrical and Electronics Engineers Inc.
Conference Proceedings Title: Proceedings of 2020 23rd International Conference on Information Fusion, FUSION 2020
ISBN: 9780578647098
DOI: 10.23919/FUSION45008.2020.9190634
Abstract
Inertial measurement units are commonly used to estimate the attitude of moving objects. Numerous nonlinear filter approaches have been proposed for solving the inherent sensor fusion problem. However, when a large range of different dynamic and static rotational and translational motions is considered, the attainable accuracy is limited by the need for situation-dependent adjustment of accelerometer and gyroscope fusion weights. We investigate to what extent these limitations can be overcome by means of artificial neural networks and how much domain-specific optimization of the neural network model is required to outperform the conventional filter solution. A diverse set of motion recordings with a marker-based optical ground truth is used for performance evaluation and comparison. The proposed neural networks are found to outperform the conventional filter across all motions only if domain-specific optimizations are introduced. We conclude that they are a promising tool for inertial-sensor-based real-time attitude estimation, but both expert knowledge and rich datasets are required to achieve top performance.
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Germany (DE)How to cite
APA:
Weber, D., Guhmann, C., & Seel, T. (2020). Neural networks versus conventional filters for inertial-sensor-based attitude estimation. In Proceedings of 2020 23rd International Conference on Information Fusion, FUSION 2020. Institute of Electrical and Electronics Engineers Inc..
MLA:
Weber, Daniel, Clemens Guhmann, and Thomas Seel. "Neural networks versus conventional filters for inertial-sensor-based attitude estimation." Proceedings of the 23rd International Conference on Information Fusion, FUSION 2020 Institute of Electrical and Electronics Engineers Inc., 2020.
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