• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.2
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• pp.189-196
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• 2019

The inertial sensor errors in SDINS(Strapdown Inertial Navigation System) can be compensated by rotating the inertial measurement unit and it is called RINS(Rotational Inertial Navigation System). It is assumed that the error of the inertial sensor in RINS is a static bias. However, the error of the inertial sensor actually developed and produced is not a static bias due to the change of the temperature applied to the sensor and the influence of the earth's gravity acceleration. In this paper, we propose a six-position gyro bias calibration method to evaluate the gyro bias required for RINS and present the test results of applying it to a ring laser gyro inertial navigation system under development.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.9
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• pp.617-625
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• 2022

This study investigates the use of movable calibration target for gyroscopic and accelerometer bias compensation of inertial measurement units for firing lane alignment. Calibration source is detected with the help of vision sensor and its information in fused with other sensors on launcher for error correction. An algorithm is proposed and tested in simulation. It has been shown that it is possible to compensate sensor biases in firing launcher in few seconds by accurately estimating the location of calibration target in inertial frame of reference.

• International Journal of Control, Automation, and Systems
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• v.2 no.4
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• pp.536-541
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• 2004

It is important to estimate and calibrate sensor errors in maintaining the performance level of SDINS. In this study, an estimation technique of fixed sensor errors for SDINS calibration is discussed. First, the fixed errors of gyros and accelerometers, excluding gyro biases are estimated by the navigation information of SDINS in multi-position. The SDINS with RLG includes flexure errors. In this study, the gyros flexures are out of consideration, but the proposed procedure selects certain positions and rotations in order to minimize the influence of flexures. Secondly, the influences of random walks, flexures and orientation errors are verified via numerical simulations. Thirdly, applying the previous estimated errors to SDINS, the estimation of gyro biases is conducted via the additional control signals of close-loop self-alignment. Lastly, the experiments illustrate that the extracted calibration parameters are available for the improvement of SDINS.

• International Journal of Aeronautical and Space Sciences
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• v.11 no.3
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• pp.234-239
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• 2010

The utilization of micro-electro-mechanical system (MEMS) gyros and accelerometers in low-level inertial measurement unit (IMU) influences cost effectiveness in a positive way under the condition that device error characteristics are fully calibrated. The conventional calibration process utilizes a rate table; however, this paper proposes a new method for achieving reference calibration data from the natural motion of pendulum to which the IMU undergoing calibration is attached. This concept was validated with experimental data. The pendulum angle measurements correlate extremely well with the solutions acquired from the pendulum equation of motion. The calibration data were computed using the regression method. The whole process was validated by comparing the measurement from the 6 sensor components with the measurements reconstructed using the identified calibration data.

• Journal of the Korea Computer Graphics Society
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• v.19 no.4
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• pp.13-22
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• 2013

A motion capture system is widely used in movies, computer game, and computer animation industries because it allows for creating realistic human motions efficiently. The inertial motion capture system has several advantages over more popular vision-based systems in terms of the required space and cost. However, it suffers from low accuracy due to the relatively high noise levels of the inertial sensors. In particular, the accelerometer used for measuring gravity direction loses the accuracy when the sensor is moving with non-zero linear acceleration. In this paper, we propose a method to remove the linear acceleration component from the accelerometer data in order to improve the accuracy of measuring gravity direction. In addition, we develop a simple method to calibrate the joint axis of a link to which an inertial sensor belongs as well as the position of a sensor with respect to the link. The calibration enables attaching inertial sensors in an arbitrary position and orientation with respect to a link.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.10
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• pp.1822-1829
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• 2008

Calibration of an MEMS inertial measurement unit is very important process for obtaining precise navigation performance. In this paper, one method is proposed to overcome a limitations on cost and efficiency using a relatively higher grade sensor and a rate table. The same dynamic input is applied to both the reference and the target sensors during and after calibration process, then the results are analyzed. The experimental results show that the proposed method is very effective and useful in practice.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1679-1683
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• 1997

In this paper a linear Kalman filter for calibration of gimballed inertial navigation system(GINS) is designed and its performace is analyzed through the simulation with a real navigation data. Simulation results show that the proposed Kalman filter gives a good performance to calibrate the sensor errors.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.7
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• pp.632-637
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• 2012

This paper introduces a simple and efficient calibration method for three-axis accelerometers and three-axis gyroscopes using three-axis motion rate table. Usually, the performance of low cost MEMS-based inertial sensors is affected by scale and bias errors significantly. The calibration of these errors is a bothersome problem, but the previous calibration methods cannot propose simple and efficient method to calibrate the errors of three-axis inertial sensors. This paper introduces a new simple and efficient method for the calibration of accelerometer and gyroscope. By using a three-axis motion rate table, this method can calibrate the accelerometer and gyroscope simultaneously and simply. Experimental results confirm the performance of the proposed method.

• International Journal of Precision Engineering and Manufacturing
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• v.9 no.3
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• pp.46-50
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• 2008

The present status and future prospects of the levitation mass method (LMM), a technique for precision mass and force measurement, are reviewed. In the LMM, the inertial force of a mass levitated using a pneumatic linear bearing is used as the reference force applied to the objects being tested, such as force transducers, materials, or structures. The inertial force of the levitated mass is measured using an optical interferometer. We have modified this technique for dynamic force calibration of impact, oscillation, and step loads. We have also applied the LMM to material testing, providing methods for evaluating material viscoelasticity under an oscillating or impact load, evaluating material friction, evaluating the biomechanics of a human hand, and generating and measuring micro-Newton-level forces.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1846-1856
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• 2016

This study proposes a novel feature point initialization method in order to improve the accuracy of feature point positions by fusing a vision sensor and a lidar. The initialization is a process that determines three dimensional positions of feature points through two dimensional image data, which has a direct influence on performance of a 6-DoF bearing-only SLAM. Prior to the initialization, an extrinsic calibration method which estimates rotational and translational relationships between a vision sensor and lidar using multiple calibration tools was employed, then the feature point initialization method based on the estimated extrinsic calibration parameters was presented. In this process, in order to improve performance of the accuracy of the initialized feature points, an iterative automatic scaling parameter tuning technique was presented. The validity of the proposed feature point initialization method was verified in a 6-DoF bearing-only SLAM framework through an indoor and outdoor tests that compare estimation performance with the previous initialization method.