Methods for in-field user calibration of an inertial measurement unit without external equipment

Fong, W.; Ong, S. K.; Nee, A. Y. C.

doi:10.1088/0957-0233/19/8/085202

Cited by 203 publications

(149 citation statements)

References 14 publications

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“…On the one hand, deterministic errors (e.g. axes misalignment) are compensated through physical models during calibration procedures and will not be treated in this article; see [1,2] for more details. On the other hand, stochastic errors contain components which have random behavior (e.g.…”

Section: Introductionmentioning

confidence: 99%

Constrained expectation-maximization algorithm for stochastic inertial error modeling: study of feasibility

Stebler¹,

Guerrier²,

Skaloud³

et al. 2011

Meas. Sci. Technol.

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Abstract. Stochastic modeling is a challenging task for low-cost sensors which errors can have complex spectral structures. This makes the tuning process of the INS/GNSS Kalman filter often sensitive and difficult. For example, first-order Gauss-Markov processes are very often used in inertial sensor models. But the estimation of their parameters is a non-trivial task if the error structure is mixed with other types of noises. Such estimation is often attempted by computing and analyzing Allan variance plots. This contribution demonstrates solving situations when the estimation of error parameters by graphical interpretation is rather difficult. The novel strategy performs direct estimation of these parameters by means of the Expectation-Maximization (EM) algorithm. The algorithm results are first analyzed with a critical and practical point of view using simulations with typically encountered error signals. These simulations show that the EM algorithm seems to perform better than the Allan variance and offers a procedure to estimate first-order Gauss-Markov processes mixed with other types of noises. At the same time, the conducted tests revealed limits of this approach that are related to the convergence and stability issues. Suggestions are given to circumvent or mitigate these problems when complexity of error structure is "reasonable". This work also highlights the fact that the suggested approach via EM algorithm and the Allan variance may not be able to estimate reasonably well parameters of complex error models, and shows the need for new estimation procedures to be developed in this context. Finally, an empirical scenario is presented to support the former findings. There, the positive effect of using the more sophisticated EM-based error modeling on a filtered trajectory is highlighted.

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Section: Introductionmentioning

confidence: 99%

Constrained expectation-maximization algorithm for stochastic inertial error modeling: study of feasibility

Stebler¹,

Guerrier²,

Skaloud³

et al. 2011

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Mathematical models for correcting the measurements of an accelerometer system have been formed in works of Skog and Händel (2006), Fong, Ong, and Nee (2008), Frosio, Pedersini, and Borghese (2009). In our present work, a mathematical calibration model (successfully tested in accelerometer sensors by Frosio, Pedersini, and Borghese 2009) has been adopted.…”

Section: Methodology For Evaluation Of Low-cost Sensorsmentioning

confidence: 99%

“…The study of the literature shows that these mechanisms can work with constant accuracy and reliability, provided that they will undergo calibration procedures involving high precision instruments (VectorNav 2016). In later reports, researches are directed to calibration methodologies that do not rely on laboratory equipment (Fong, Ong, and Nee 2008;Frosio, Pedersini, and Borghese 2009;Skog and Händel 2006). The only way to ensure the sensors system efficiency, in achieving the required accuracy, is to perform an evaluation precision control.…”

Section: Introductionmentioning

confidence: 99%

A methodology for the performance evaluation of low-cost accelerometer and magnetometer sensors in geomatics applications

Patonis

Πατιάς

Tziavos

et al. 2018

Geo-spatial Information Science

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This paper presents a methodology and its software implementation for the performance evaluation of low-cost accelerometer and magnetometer sensors for use in geomatics applications. A known mathematical calibration model has been adopted. The method was completed with statistical methodologies for adjusting observations and has been extended to calculate accuracies for the attitude, heading, and tilt angles estimation that are of interest to geomatics applications. The evaluation method consists of two stages. First, the evaluation method reviews the total magnitude of acceleration or the strength of the magnetic field. Second, the evaluation is more detailed and concerns the determination of mathematical parameters that describe both accelerometer and magnetometer working model. A software tool that implements the evaluation model has been developed and is applied both in accelerometer and magnetometer measurement data-sets acquired from a low-cost sensor system.

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“…The depth-sensing camera enables the tablet to capture a point cloud (PC), or a set of 3D points, that represents objects within its field of view. [23][24][25] The phantom was placed in the treatment position within the room and scanned with the tablet. The scan was complete by capturing frames from the depth camera while rotating tablet approximately 300…”

Section: A 3d Model Generationmentioning

confidence: 99%

A depth‐sensing technique on 3D‐printed compensator for total body irradiation patient measurement and treatment planning

et al. 2016

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Purpose: The purpose of total body irradiation (TBI) techniques is to deliver a uniform radiation dose to the entire volume of a patient's body. Due to variations in the thickness of the patient, it is difficult to produce such a uniform dose distribution throughout the body. In many techniques, a compensator is used to adjust the dose delivered to various sections of the patient. The current study aims to develop and validate an innovative method of using depth-sensing cameras and 3D printing techniques for TBI treatment planning and compensator fabrication. Methods: A tablet with an integrated depth-sensing camera and motion tracking sensors was used to scan a RANDO™ phantom positioned in a TBI treatment booth to detect and store the 3D surface in a point cloud format. The accuracy of the detected surface was evaluated by comparing extracted body thickness measurements with corresponding measurements from computed tomography (CT) scan images. The thickness, source to surface distance, and off-axis distance of the phantom at different body section were measured for TBI treatment planning. A detailed compensator design was calculated to achieve a uniform dose distribution throughout the phantom. The compensator was fabricated using a 3D printer, silicone molding, and a mixture of wax and tungsten powder. In vivo dosimetry measurements were performed using optically stimulated luminescent detectors. Results: The scan of the phantom took approximately 30 s. The mean error for thickness measurements at each section of phantom relative to CT was 0.48 ± 0.27 cm. The average fabrication error for the 3D-printed compensator was 0.16 ± 0.15 mm. In vivo measurements for an end-to-end test showed that overall dose differences were within 5%. Conclusions: A technique for planning and fabricating a compensator for TBI treatment using a depth camera equipped tablet and a 3D printer was demonstrated to be sufficiently accurate to be considered for further investigation. C

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Methods for in-field user calibration of an inertial measurement unit without external equipment

Cited by 203 publications

References 14 publications

Constrained expectation-maximization algorithm for stochastic inertial error modeling: study of feasibility

Constrained expectation-maximization algorithm for stochastic inertial error modeling: study of feasibility

A methodology for the performance evaluation of low-cost accelerometer and magnetometer sensors in geomatics applications

A depth‐sensing technique on 3D‐printed compensator for total body irradiation patient measurement and treatment planning

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