Towards orientation estimation using a loosely attached IMU with a recurrent neural network

Master thesis (2021)

Authors

S.J. van den Heuvel Mechanical Engineering

Contributors

M. Kok Team Jan-Willem van Wingerden - Mechanical, Maritime and Materials Engineering (mentor)
Raf Van de Plas Team Raf Van de Plas - Mechanical, Maritime and Materials Engineering (graduation committee member)
Faculty
Mechanical Engineering, Mechanical Engineering
Copyright: © 2021 Stefan van den Heuvel
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Published Date

27-05-2021

Language

English

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Faculty

Mechanical Engineering

Abstract

Inertial measurement units (IMUs) are getting more and more incorporated into our lives due to their improving accuracy, lower cost and smaller sizes. Applications for inertial-based orientation estimation can already be found in the field of computer vision, aerospace engineering, robotics, navigation, biomechanics, health monitoring and sports. In order to enable non-intrusive, long-term tracking, this study addresses the problem of orientation estimation using a loosely attached IMU. To this end, a hybrid model is proposed combining
classical orientation methods with machine learning (ML) techniques. Previous works have focused only on parts of this task, which is why to date, there are little to no solutions to this problem. In this study, a model is designed containing three steps that are shown to be beneficial for reducing the estimation error. With the first step, the model becomes robust to a misaligned mounting of the loosely attached IMU and it allows to identify which part of the error can be contributed to the average misalignment. For the second step, two
algorithms are presented which make the model invariant to the absolute heading direction of the recorded data. In the third step, the data is prepared such that the size of the search space is reduced by a factor 2. Therefore, less data is required for learning the problem or, with an equal amount of data, a bigger part of the process can be discovered. Besides these steps, it is shown that the use of physics-based knowledge in the form of orientation estimates,
as opposed to raw inertial data as input features for the ML method, is beneficial.