A New Perspective on Low-Cost MEMS-Based AHRS Determination

Navidi, Néda; Landry, René

doi:10.3390/s21041383

Cited by 6 publications

(5 citation statements)

References 21 publications

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“…Sensorfusion deals with the processing and combination of individual sensor data to increase the quality of output information. Typically, sensor-fusion is dealing with three-dimensional inertial measurements from accelerometer and gyroscope sensors (6-DoF IMU-data), which are combined in order to return a single measurement of orientation within the attitude and heading reference system (AHRS) that describes the orientation of a rigid object in three-dimensional space [60]. The sensor fusion algorithm is founded on the principle of gyroscopic integration for initial orientation estimates, while sensor data from accelerometer are needed for the reduction of drift (error of orientation estimates from sensor data integration) due to noise and biases [12,51].…”

Section: Sensor-fusion Using Imu-datamentioning

confidence: 99%

SmartWood: field-based analysis of large wood movement dynamics using inertial measurement units (IMUs)

Spreitzer,

Schalko,

Boes

et al. 2024

Environ Sci Eur

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Wood plays an important ecological role in rivers. Yet challenges arise when large wood (LW) is mobilised and transported during floods. Due to a lack of quantitative data, movement behaviour of LW during floods is still not well understood to date. A proof-of-concept study was conducted at three Swiss rivers to test state-of-the-art sensor-tagged logs, so-called “SmartWood” and collect quantitative field-scale data about LW movement behaviour. The experiments utilised innovative inertial measurement units (IMUs), which have been developed at the Laboratory of Hydraulics, Hydrology and Glaciology (VAW) at ETH Zurich and implanted into wood logs (SmartWood) at prototype scale. Each IMU comprised three individual sensors (gyroscope, accelerometer, and magnetometer) and was equipped with an on-board processor, an AA battery (4.35 V), a memory (8 MB), and a Wi-Fi transmitter (100 m) for data transfer. After successful initial verification tests of the sensors, the IMUs were installed into debranched wood logs, measuring 4.35 m in length and 0.33 m in diameter. At the time of the field experiments, each SmartWood-log weighted between 170 and 220 kg, yielding a density of roughly 500 kg∙m−3. At the Limmat, Thur, and Grosse Melchaa Rivers in Switzerland, innovative yet discontinuous data were obtained. Results revealed consistent movement dynamics across all field sites. Specifically, we observed positive yaw movement during transport of SmartWood along the left river bank and negative yaw movement along the right river bank. Furthermore, interactions of SmartWood with channel boundaries, riparian vegetation, and objects (e.g., ferry dock) were registered and quantified, even when the SmartWood-log was transported out of sight of traditional sensing methods. The conducted field experiments enabled the initial testing of SmartWood in the field and exposed critical limitations of the IMUs and software algorithms for the reconstruction and analysis of floating LW dynamics. The gained knowledge and introduced sensing method will benefit the quantitative assessment of LW dynamics in rivers to maintain safety and functionality for instream structures (e.g., considering LW movement dynamics for the robust design of LW retention and guiding structures), but also river restoration projects and numerical models that rely on quantitative field-scale data.

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Section: Sensor-fusion Using Imu-datamentioning

confidence: 99%

SmartWood: field-based analysis of large wood movement dynamics using inertial measurement units (IMUs)

Spreitzer,

Schalko,

Boes

et al. 2024

Environ Sci Eur

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“…The quality of IMU systems then decreases until they reach the other end of the scale, the consumer-grade systems, typically used for gaming or automotive applications [13][14][15][16]. Typically, these sensors are made of MEMS technology, which achieves significant improvements in size, weight, and cost [17].…”

Section: Introductionmentioning

confidence: 99%

The Use of Artificial Intelligence Approaches for Performance Improvement of Low-Cost Integrated Navigation Systems

Alteriis

Ruggiero

Prete

et al. 2023

Sensors

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In this paper, the authors investigate the possibility of applying artificial intelligence algorithms to the outputs of a low-cost Kalman filter-based navigation solution in order to achieve performance similar to that of high-end MEMS inertial sensors. To further improve the results of the prototype and simultaneously lighten filter requirements, different AI models are compared in this paper to determine their performance in terms of complexity and accuracy. By overcoming some known limitations (e.g., sensitivity on the dimension of input data from inertial sensors) and starting from Kalman filter applications (whose raw noise parameter estimates were obtained from a simple analysis of sensor specifications), such a solution presents an intermediate behavior compared to the current state of the art. It allows the exploitation of the power of AI models. Different Neural Network models have been taken into account and compared in terms of measurement accuracy and a number of model parameters; in particular, Dense, 1-Dimension Convolutional, and Long Short Term Memory Neural networks. As can be excepted, the higher the NN complexity, the higher the measurement accuracy; the models’ performance has been assessed by means of the root-mean-square error (RMSE) between the target and predicted values of all the navigation parameters.

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“…Roetenberg et al [20] used an IMU with a magnetometer for orientation estimation of human body segments with a complementary Kalman filter. Again, Navidi and Landry [21] used a low-cost IMU with a magnetometer for orientation estimation by a complementary adaptive Kalman filter. In this work, the authors proposed an initialization method which, because of the nonlinear nature of the system, is one important step in attitude and heading determination.…”

Section: Introductionmentioning

confidence: 99%

9-DOF IMU-Based Attitude and Heading Estimation Using an Extended Kalman Filter with Bias Consideration

Farahan

Machado

Almeida

et al. 2022

Sensors

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The attitude and heading reference system (AHRS) is an important concept in the area of navigation, image stabilization, and object detection and tracking. Many studies and works have been conducted in this regard to estimate the accurate orientation of rigid bodies. In most research in this area, low-cost MEMS sensors are employed, but since the system’s response will diverge over time due to integration drift, it is necessary to apply proper estimation algorithms. A two-step extended Kalman Filter (EKF) algorithm is used in this study to estimate the orientation of an IMU. A 9-DOF device is used for this purpose, including a 6-DOF IMU with a three-axis gyroscope and a three-axis accelerometer, and a three-axis magnetometer. In addition, to have an accurate algorithm, both IMU and magnetometer biases and disturbances are modeled and considered in the real-time filter. After applying the algorithm to the sensor’s output, an accurate orientation as well as unbiased angular velocity, linear acceleration, and magnetic field were achieved. In order to demonstrate the reduction of noise power, fast Fourier transform (FFT) diagrams are used. The effect of the initial condition on the response of the system is also investigated.

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A New Perspective on Low-Cost MEMS-Based AHRS Determination

Cited by 6 publications

References 21 publications

SmartWood: field-based analysis of large wood movement dynamics using inertial measurement units (IMUs)

SmartWood: field-based analysis of large wood movement dynamics using inertial measurement units (IMUs)

The Use of Artificial Intelligence Approaches for Performance Improvement of Low-Cost Integrated Navigation Systems

9-DOF IMU-Based Attitude and Heading Estimation Using an Extended Kalman Filter with Bias Consideration

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