A Fast and Accurate Accelerometer and Magnetometer Alignment Algorithm

Papafotis, Konstantinos; Sotiriadis, Paul P.

doi:10.1109/jsen.2020.3011579

Cited by 9 publications

(6 citation statements)

References 14 publications

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“…After finding the intrinsic calibration, the extrinsic calibration between the IMU and magnetometer as well as the local magnetic inclination can be found. This is done by applying the method outlined in Papafotis and Sotiriadis (2020). Individual data sequences were recorded which This figure shows the assembly of the primary IMU sensor within the PixHawk autopilot, on top of a damping layer that acts as a low-pass filter between the IMU and the main PCB board.…”

Section: Sensorsmentioning

confidence: 99%

“…After finding the intrinsic calibration, the extrinsic calibration between the IMU and magnetometer as well as the local magnetic inclination can be found. This is done by applying the method outlined in Papafotis and Sotiriadis (2020). Individual data sequences were recorded which provide static rotations of the vehicle in all six rotational directions with respect to the gravity vector.…”

Section: System Setupmentioning

confidence: 99%

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The INSANE dataset: Large number of sensors for challenging UAV flights in Mars analog, outdoor, and out-/indoor transition scenarios

Brommer,

Fornasier,

Scheiber

et al. 2024

The International Journal of Robotics Research

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For real-world applications, autonomous mobile robotic platforms must be capable of navigating safely in a multitude of different and dynamic environments with accurate and robust localization being a key prerequisite. To support further research in this domain, we present the INSANE datasets (Increased Number of Sensors for developing Advanced and Novel Estimators)—a collection of versatile Micro Aerial Vehicle (MAV) datasets for cross-environment localization. The datasets provide various scenarios with multiple stages of difficulty for localization methods. These scenarios range from trajectories in the controlled environment of an indoor motion capture facility, to experiments where the vehicle performs an outdoor maneuver and transitions into a building, requiring changes of sensor modalities, up to purely outdoor flight maneuvers in a challenging Mars analog environment to simulate scenarios which current and future Mars helicopters would need to perform. The presented work aims to provide data that reflects real-world scenarios and sensor effects. The extensive sensor suite includes various sensor categories, including multiple Inertial Measurement Units (IMUs) and cameras. Sensor data is made available as unprocessed measurements and each dataset provides highly accurate ground truth, including the outdoor experiments where a dual Real-Time Kinematic (RTK) Global Navigation Satellite System (GNSS) setup provides sub-degree and centimeter accuracy (1-sigma). The sensor suite also includes a dedicated high-rate IMU to capture all the vibration dynamics of the vehicle during flight to support research on novel machine learning-based sensor signal enhancement methods for improved localization. The datasets and post-processing tools are available at: https://sst.aau.at/cns/datasets/insane-dataset/

show abstract

Section: Sensorsmentioning

confidence: 99%

“…After finding the intrinsic calibration, the extrinsic calibration between the IMU and magnetometer as well as the local magnetic inclination can be found. This is done by applying the method outlined in Papafotis and Sotiriadis (2020). Individual data sequences were recorded which provide static rotations of the vehicle in all six rotational directions with respect to the gravity vector.…”

Section: System Setupmentioning

confidence: 99%

The INSANE dataset: Large number of sensors for challenging UAV flights in Mars analog, outdoor, and out-/indoor transition scenarios

Brommer,

Fornasier,

Scheiber

et al. 2024

The International Journal of Robotics Research

View full text Add to dashboard Cite

show abstract

“…After finding the intrinsic calibration, the extrinsic calibration between IMU and magnetometer as well as the local magnetic inclination can be found. This is done by applying the method outlined in [24]. Individual data sequences were recorded which provide static rotations of the vehicle in all six rotational directions with respect to the gravity vector.…”

Section: B Sensorsmentioning

confidence: 99%

“…Individual data sequences were recorded which provide static rotations of the vehicle in all six rotational directions with respect to the gravity vector. This data is processed with the cost function described in [24] to find the most accurate rotation between the gravity and magnetic vectors, including the magnetic inclination. The data set is published with a default calibration for the vehicle setup and with a set of tools to recompute the calibration with potentially different parameters.…”

Section: B Sensorsmentioning

confidence: 99%

INSANE: Cross-Domain UAV Data Sets with Increased Number of Sensors for developing Advanced and Novel Estimators

Brommer¹,

Fornasier²,

Scheiber³

et al. 2022

Preprint

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For real-world applications, autonomous mobile robotic platforms must be capable of navigating safely in a multitude of different and dynamic environments with accurate and robust localization being a key prerequisite. To support further research in this domain, we present the INSANE data sets -a collection of versatile Micro Aerial Vehicle (MAV) data sets for cross-environment localization. The data sets provide various scenarios with multiple stages of difficulty for localization methods. These scenarios range from trajectories in the controlled environment of an indoor motion capture facility, to experiments where the vehicle performs an outdoor maneuver and transitions into a building, requiring changes of sensor modalities, up to purely outdoor flight maneuvers in a challenging Mars analog environment to simulate scenarios which current and future Mars helicopters would need to perform. The presented work aims to provide data that reflects real-world scenarios and sensor effects. The extensive sensor suite includes various sensor categories, including multiple Inertial Measurement Units (IMUs) and cameras. Sensor data is made available as raw measurements and each data set provides highly accurate ground truth, including the outdoor experiments where a dual Real-Time Kinematic (RTK) Global Navigation Satellite System (GNSS) setup provides subdegree and centimeter accuracy (1-sigma). The sensor suite also includes a dedicated high-rate IMU to capture all the vibration dynamics of the vehicle during flight to support research on novel machine learning-based sensor signal enhancement methods for improved localization. The data sets and post-processing tools are available at: https://sst.aau.at/cns/datasets

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“…The proposed algorithm applies when the two sensors are fixed on the same rigid platform and individually calibrated. It has been shown that this method is 50 times faster than the Newton-Raphson method or the gradient descent method [17]. Wang, P and Gao, Y presented a new in-field recalibration method for three-axial accelerometer based on beetle swarm antenna search algorithm.…”

Section: Introductionmentioning

confidence: 99%

Calibration of accelerometer and magnetometer sensors of MWD systems in directional drilling application

Malekizadeh,

Afarideh,

Mohamadian

et al. 2024

Meas. Sci. Technol.

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Measurement While Drilling (MWD) tools include accelerometers, magnetometers and temperature sensors. The accuracy of these sensors are highly important in measuring Inclination and Azimuth angle parameters in a directional well. These factors can increase costs when drilling a directional well. During the past years, many efforts have been made to increase the accuracy of these tools. Accuracy of accelerometer and magnetometer sensors are influenced by scale, bias and non-orthogonal errors. Considering that MWD systems work in a wide range of temperatures (for example 0-150 °C), the temperature parameter should also be added to the factors affecting the errors. Here, after making a sample of MWD tool, a method to calibrate the tool using mathematical model based on Total Field Calibration(TFC) algorithm, least square method and the transfer matrix is presented. Calibrated parameters at ambient temperature are calculated with acceptable speed. Then, a method for calculating temperature coefficients in the desired range is provided. Compared to the case where temperature coefficients are not considered, the obtained results show a significantly increased accuracy of Inclination and Azimuth parameters through the use of these coefficients.

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A Fast and Accurate Accelerometer and Magnetometer Alignment Algorithm

Cited by 9 publications

References 14 publications

The INSANE dataset: Large number of sensors for challenging UAV flights in Mars analog, outdoor, and out-/indoor transition scenarios

The INSANE dataset: Large number of sensors for challenging UAV flights in Mars analog, outdoor, and out-/indoor transition scenarios

INSANE: Cross-Domain UAV Data Sets with Increased Number of Sensors for developing Advanced and Novel Estimators

Calibration of accelerometer and magnetometer sensors of MWD systems in directional drilling application

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