• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.1
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• pp.41-48
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• 2019

This paper deals with a method of aligning an aircraft fuselage and an inertial navigation sensor using three-dimensional coordinates obtained by an optical method. In order to verify the feasibility, we introduce the method to accurately align the coordinate system of the inertial navigation sensor and the aircraft reference coordinate system. It is verified through simulation that reflects the error level of the measuring device. In addition, optimization method based alignment algorithm is proposed for connection between optical sensor and inertial navigation sensor.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.8
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• pp.3011-3024
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• 2021

Pose estimation of the sensor is important issue in many applications such as robotics, navigation, tracking, and Augmented Reality. This paper proposes visual-inertial integration system appropriate for dynamically moving condition of the sensor. The orientation estimated from Inertial Measurement Unit (IMU) sensor is used to calculate the essential matrix based on the intrinsic parameters of the camera. Using the epipolar geometry, the outliers of the feature point matching are eliminated in the image sequences. The pose of the sensor can be obtained from the feature point matching. The use of IMU sensor can help initially eliminate erroneous point matches in the image of dynamic scene. After the outliers are removed from the feature points, these selected feature points matching relations are used to calculate the precise fundamental matrix. Finally, with the feature point matching relation, the pose of the sensor is estimated. The proposed procedure was implemented and tested, comparing with the existing methods. Experimental results have shown the effectiveness of the technique proposed in this paper.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.10
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• pp.1746-1754
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• 2016

In this paper, we provide a development and evaluation method for an archery posture analyzing system, using an inertial sensor. The system was developed using LabVIEW2014 by National Instruments and evaluated using the DTW algorithm. To convert the voltage value of the inertial sensor into a physical value, a coordinate transformation matrix bias was applied. To evaluate the similarity of movement in archery shooting, the DTW distance was calculated and similarity was confirmed based on simple mechanical movement, the same person's shooting movement, shooting movement with another person, and the noise signal. The average similarity comparison results were as follows: simple mechanical movement was 17.05%, the same person's shooting movement was 26.48%, shooting movement with another person was 62.8%, and the noise signal was 328.5%; a smaller value indicates a higher level of similarity. We confirmed the possibility of analyzing the archery posture using 3-axis acceleration of the inertial sensor. We inferred that the proposed method might be important means for assessing shooting skills, evaluation of archer's progress, and finding talented archers in advance.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1144-1148
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• 1996

This paper presents WMR modelling and path tracking algorithm using Inertial Navigation System. The error models of gyroscope and accelerometers in INS are derived by Gauss-Newton method which is nonlinear regression model. Then, to test availability of error model, we pursue the fitness diagnosis about probability characteristic for real data and estimated data. Performance of inertial sensor with error model and Kalman filter is pursued by comparing with one without them. The computer simulation shows that position error remarkably decrease when error compensation is applied.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.544-546
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• 2004

This paper designs and evaluates a complementary filter for fusion of inertial sensor signals. Specifically, the designed filter is applied to inverted pendulum control where the pendulum's angle information is obtained from low-cost tilt and gyroscope sensors instead of an optical encoder. The complementary filter under consideration is a conventional one which consists of low- and high-pass filters. However, to improve the performance of the filter on the gyroscope, we use an integrator in the filter's outer loop. Frequency responses are obtained with both tilt and gyroscope sensors. Based on the frequency response results, we determine appropriate parameter values for the filter. The performance of the designed complementary filter is evaluated by applying the filter to inverted pendulum control. Experiments show that the performance of the designed filter is comparable to that of an optical encoder and low-cost inertial sensors can be used for inverted pendulum control with the heir of sensor fusion.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2540-2542
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• 2005

This paper proposed the new HILS technique called Injection Method for Inertial Sensor Verification of high maneuvering missiles. The performance of Inertial Sensor is verified by Injection Method and traditional FMS Method. After all, Injection Method for Inertial Sensor has capability for various scenario and critical condition test.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.2
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• pp.179-185
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• 2015

Although an accelerometer is a sensor that measures acceleration, it cannot be used by itself to measure the acceleration when the orientation of the sensor changes. This paper introduces a Kalman filter for the estimation of a sensor acceleration based on a six-axis inertial sensor (i.e., a three-axis accelerometer and three-axis gyroscope). The novelty of the proposed Kalman filter lies in the fact that its state vector includes not only the tilt angle variable but also the sensor acceleration. Thus, the filter can explicitly estimate the latter with a high accuracy. The accuracy of acceleration estimates were validated experimentally under three different dynamic conditions, using an optical motion capture system. It could be concluded that the performance of the proposed Kalman filter was comparable to that of the state-of-the-art estimation algorithm employed by the Xsens MTw. The proposed algorithm may be more suitable than inertial/magnetic sensor-based algorithms for various applications adopting six-axis inertial sensors.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2008.11a
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• pp.495-496
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• 2008

• Physical Therapy Rehabilitation Science
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• v.11 no.3
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• pp.311-320
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• 2022

Objective: To investigate the reliability and validity of static balance measurements using an acceleration sensor and a gyroscope sensor in smart phone inertial sensors. Design: Equivalent control group pretest-posttest. Methods: Subjects were forty five healthy adults aged twenty to fifty-years-old who had no disease that could affect the experiment. After pre-test, all participants wore a waist band with smart phone, and conducted six static balance measurements on the force plate twice for 35 seconds each. To investigate the test-retest reliability of both smart phone inertial sensors, we compared the intra-correlation coefficient (ICC 3, 1) between primary and secondary measurements with the calculated root mean scale-total data. To determine the validity of the two sensors, it was measured simultaneously with force plate, and the comparision was done by Pearson's correlation. Results: The test-retest reliability showed excellent correlation for acceleration sensor, and it also showed excellent to good correlation for gyroscope sensor(p<0.05). The concurrent validity of smartphone inertial sensors showed a mostly poor to fair correlation for tandem-stance and one-leg-stance (p<0.05) and unacceptable correlation for the other postures (p>0.05). The gyroscope sensor showed a fair correlation for most of the RMS-Total data, and the other data also showed poor to fair correlation (p<0.05). Conclusions: The result indicates that both acceleration sensor and gyroscope sensor has good reliability, and that compared to force plate, acceleration sensor has unacceptable or poor correlation, and gyroscope sensor has mostly fair correlation.