On orientation estimation using iterative methods in Euclidean space

Skoglund, Martin A.; Sjanic, Zoran; Kok, Manon

doi:10.23919/icif.2017.8009830

Cited by 7 publications

(4 citation statements)

References 19 publications

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“…As can be seen in ( 15), we directly estimate the angular velocity which is subsequently used to update the orientation. This has close connections to the approaches discussed in [28,29].…”

Section: Resulting Algorithmsupporting

confidence: 63%

A Fast and Robust Algorithm for Orientation Estimation Using Inertial Sensors

Kok

Schön

2019

IEEE Signal Process. Lett.

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We present a novel algorithm for online, real-time orientation estimation. Our algorithm integrates gyroscope data and corrects the resulting orientation estimate for integration drift using accelerometer and magnetometer data. This correction is computed, at each time instance, using a single gradient descent step with fixed step length. This fixed step length results in robustness against model errors, e.g. caused by large accelerations or by short-term magnetic field disturbances, which we numerically illustrate using Monte Carlo simulations. Our algorithm estimates a three-dimensional update to the orientation rather than the entire orientation itself. This reduces the computational complexity by approximately 1/3 with respect to the state of the art. It also improves the quality of the resulting estimates, specifically when the orientation corrections are large. We illustrate the efficacy of the algorithm using experimental data.

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“…As can be seen in ( 15), we directly estimate the angular velocity which is subsequently used to update the orientation. This has close connections to the approaches discussed in [28,29].…”

Section: Resulting Algorithmsupporting

confidence: 63%

A Fast and Robust Algorithm for Orientation Estimation Using Inertial Sensors

Kok

Schön

2019

IEEE Signal Process. Lett.

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“…In this section, we will derive an algorithm to obtain filtering estimates of the orientation using Gauss-Newton optimization. The resulting algorithm is closely related to the iterated multiplicative extended Kalman filter presented in [142].…”

Section: Filtering Estimates Of the Orientation Using Optimizationmentioning

confidence: 99%

Using Inertial Sensors for Position and Orientation Estimation

Kok

Hol

Schön

2017

FNT in Signal Processing

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309

287

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In recent years, microelectromechanical system (MEMS) inertial sensors (3D accelerometers and 3D gyroscopes) have become widely available due to their small size and low cost. Inertial sensor measurements are obtained at high sampling rates and can be integrated to obtain position and orientation information. These estimates are accurate on a short time scale, but suffer from integration drift over longer time scales. To overcome this issue, inertial sensors are typically combined with additional sensors and models. In this tutorial we focus on the signal processing aspects of position and orientation estimation using inertial sensors. We discuss different modeling choices and a selected number of important algorithms. The algorithms include optimization-based smoothing and filtering as well as computationally cheaper extended Kalman filter and complementary filter implementations. The quality of their estimates is illustrated using both experimental and simulated data.

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“…See [21] for further details. To obtain estimates of the orientation the IMU currently uses a Mahony filter [22], although other filters might be more suitable in future setups.…”

Section: Array Orientation Using Imu and Magnetometermentioning

confidence: 99%

Sound Source Localization and Reconstruction Using a Wearable Microphone Array and Inertial Sensors

Veiback

Skoglund

Gustafsson

et al. 2020

2020 IEEE 23rd International Conference on Information Fusion (FUSION)

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A wearable microphone array platform is used to localize stationary sound sources and amplify the sound in the desired directions using several beamforming methods. The platform is equipped with inertial sensors and a magnetometer allowing predictions of source locations during orientation changes and compensation for the displacement in the array configuration. The platform is modular, open and 3D printed to allow for easy reconfiguration of the array and for reuse in other applications, e.g., mobile robotics. The software components are based on open source. A new method for source localization and signal reconstruction using Taylor expansion of the signals is proposed. This and various standard and non-standard Direction of Arrival (DOA) methods are evaluated in simulation and experiments with the platform to track and reconstruct multiple and single sources. Results show that sound sources can be localized and tracked robustly and accurately while rotating the platform and that the proposed method outperforms standard methods at reconstructing the signals.

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On orientation estimation using iterative methods in Euclidean space

Cited by 7 publications

References 19 publications

A Fast and Robust Algorithm for Orientation Estimation Using Inertial Sensors

A Fast and Robust Algorithm for Orientation Estimation Using Inertial Sensors

Using Inertial Sensors for Position and Orientation Estimation

Sound Source Localization and Reconstruction Using a Wearable Microphone Array and Inertial Sensors

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