• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.1
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• pp.121-126
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• 2017

Tracking system is essential for Image Guided Surgery(IGS). Optical Tracking System(OTS) is widely used to IGS for its high accuracy and easy usage. However, OTS doesn't work when occlusion of marker occurs. In this paper sensor data fusion with OTS and Inertial Navigation System(INS) is proposed to solve this problem. The proposed system improves the accuracy of tracking system by eliminating gaussian error of the sensor and supplements the disadvantages of OTS and IMU through sensor fusion based on Kalman filter. Also, sensor calibration method that improves the accuracy is introduced. The performed experiment verifies the effectualness of the proposed algorithm.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.32 no.10
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• pp.53-59
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• 2004

A calibration technique for a redundant IMU with low-grade inertial sensors is proposed. In order to calibrate the redundant IMU that can detect and isolate a faulty sensor, the fundamental coordinate frames in the IMU are defined and the IMU error is modeled based on the frames. Equations to estimate the error coefficients of the redundant IMU are formulated, and a test sequence using the 2-axis rate table is also presented. Finally, a redundant IMU with cone configuration is implemented using the low-grade inertial sensors and the performance of the proposed technique is verified by some experiments.

• Journal of Positioning, Navigation, and Timing
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• v.13 no.2
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• pp.179-187
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• 2024

The performance of a Strapdown Inertial Navigation System (SDINS) relies heavily on the accuracy of sensor error calibration. Systematic calibration is usually employed when only a 2-axis turntable is available. For systematic calibration, the body frame is commonly defined with respect to sensor axes for ease of computation. The drawback of this approach is that sensor axes may undergo time-varying deflection under temperature change, causing pseudo gyro bias. The effect of pseudo gyro bias on navigation performance is negligible for low grade navigation systems. However, for higher grade systems undergoing rapid temperature change, the error is no longer negligible. This paper describes in detail conditions leading to the presence of pseudo gyro bias, and proposes two techniques for mitigating the error. Experimental results show that applying these techniques improves navigation performance for precision SDINS, especially under rapid temperature change.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.664-667
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• 1996

This paper presents the error parameter estimation technique for IMU(Inertial Measurement Unit) which is core sensor of INS(Inertial Navigation System) and verifies it via laboratory test. Firstly the error characteristic of gyroscope and accelerometer which is contained in IMU is examined and the error modelling is executed. The error of IMU can be divided into deterministic and random part, and the deterministic error can be divided into static and dynamic part. This paper consider the random part as constant. Secondly the error parameter estimation technique and following procedure for laboratory test is explained. Thirdly according to the test procedure the IMU test for static error is executed using 2-axis rate table and estimation result is presented with discussion about its validity.

• Ocean Systems Engineering
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• v.7 no.2
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• pp.157-177
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• 2017

This research is based on the concept of safety airbag to design a self-rescue system for the autonomous underwater vehicle (AUV) using micro inertial sensing module. To reduce the possibility of losing the underwater vehicle and the difficulty of searching and rescuing, when the AUV self-rescue system (ASRS) detects that the AUV is crashing or encountering a serious collision, it can pump carbon dioxide into the airbag immediately to make the vehicle surface. ASRS consists of 10-DOF sensing module, sensing attitude algorithm and air-pumping mechanism. The attitude sensing modules are a nine-axis micro-inertial sensor and a barometer. The sensing attitude algorithm is designed to estimate failure attitude of AUV properly using sensor calibration and extended Kalman filter (SCEKF), feature extraction and backpropagation network (BPN) classify. SCEKF is proposed to be used subsequently to calibrate and fuse the data from the micro-inertial sensors. Feature extraction and BPN training algorithms for classification are used to determine the activity malfunction of AUV. When the accident of AUV occurred, the ASRS will immediately be initiated; the airbag is soon filled, and the AUV will surface due to the buoyancy. In the future, ASRS will be developed successfully to solve the problems such as the high losing rate and the high difficulty of the rescuing mission of AUV.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.4
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• pp.518-524
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• 2011

This paper presents a study on the calibration of accelerometer data in the gyroscope free inertial navigation system(GFINS) using fuzzy inference system(FIS). The conventional INS(inertial navigation system) which can measure yaw rate and linear velocity using inertial sensors as the gyroscope and accelerometer. However, the INS is difficult to design as small size and low power because it uses the gyroscope. To solve the problem, the GFINS which does not have the gyroscope have been studied actively. However, the GFINS has cumulative error problem still. Hence, this paper proposes Fuzzy-GFINS which can calibrate the data of an accelerometer using FIS consists of two inputs that are ratio between linear velocity of the autonomous ground vehicle(AGV) and the accelerometer and ratio between linear velocity of the encoders and the accelerometer. To evaluate the proposed Fuzzy-GFINS, we made the AGV with Mecanum wheels and applied the proposed Fuzzy-GFINS. In experimental result, we verified that the proposed method can calibrate effectively data of the accelerometer in the GFINS.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.8
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• pp.774-778
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• 2008

The inaccuracy of inertial sensors used in low cost IMU's limits the usage to ARS, at best. Sensor fusion technologies are widely used to overcome this problem. GPS is the most popular secondary sensor, but GPS alone cannot fully compensate the IMU errors in the initial alignment process and rectilinear flights. This paper presents a new concept of aiding the low cost IMU with the sun line of sight vector. The simulation and experimental results in this paper proves that aiding of INS/GPS with the sun line of sight vector increases the observability and improves accuracy remarkably.

• International Journal of Control, Automation, and Systems
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• v.4 no.3
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• pp.351-358
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• 2006

The proposed inertial measurement unit (IMU) is composed of accelerometers only. It can determine a vehicle's position and attitude, which is the Gyro-free INS. The Gyro-free INS error is deeply affected by the sensor bias, scale factor and misalignment. However, these parameters can be obtained in the laboratory. After these misalignments are corrected, the Gyro-free strap-down INS could be more accurate. This paper presents a compensator design for the strap-down six-accelerometer INS to correct misalignment. A calibration experiment is taken to get the error parameters. A simulation results show that it will decrease the INS error to enhance the performance after compensation.

• Journal of Sensor Science and Technology
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• v.27 no.2
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• pp.86-92
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• 2018

In recent times, motion capture technology using inertial measurement unit (IMU) sensors has been actively used in sports. In this study, we developed a canoe paddle, installed with an IMU and a water level sensor, as a system tool for training and calibration purposes in water sports. The hardware was fabricated to control an attitude heading reference system (AHRS) module, a water level sensor, a communication module, and a wireless charging circuit. We also developed an application program for the mobile device that processes paddling motion data from the paddling operation and also visualizes it. An AHRS module with acceleration, gyro, and geomagnetic sensors each having three axes, and a resistive water level sensor that senses the immersion depth in the water of the paddle represented the paddle motion. The motion data transmitted from the paddle device is internally decoded and classified by the application program in the mobile device to perform visualization and to operate functions of the mobile training/correction system. To conclude, we tried to provide mobile knowledge service through paddle sport data using this technique. The developed system works reasonably well to be used as a basic training and posture correction tool for paddle sports; the transmission delay time of the sensor system is measured within 90 ms, and it shows that there is no complication in its practical usage.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1125-1139
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• 2022

In an unmanned aerial vehicles (UAVs) system, a physical offset can be existed between the global positioning system/inertial measurement unit (GPS/IMU) sensor and the observation sensor such as a hyperspectral sensor, and a lidar sensor. As a result of the physical offset, a misalignment between each image can be occurred along with a flight direction. In particular, in a case of multi-sensor system, an observation sensor has to be replaced regularly to equip another observation sensor, and then, a high cost should be paid to acquire a calibration parameter. In this study, we establish a precise sensor model equation to apply for a multiple sensor in common and propose an independent physical offset estimation method. The proposed method consists of 3 steps. Firstly, we define an appropriate rotation matrix for our system, and an initial sensor model equation for direct-georeferencing. Next, an observation equation for the physical offset estimation is established by extracting a corresponding point between a ground control point and the observed data from a sensor. Finally, the physical offset is estimated based on the observed data, and the precise sensor model equation is established by applying the estimated parameters to the initial sensor model equation. 4 region's datasets(Jeon-ju, Incheon, Alaska, Norway) with a different latitude, longitude were compared to analyze the effects of the calibration parameter. We confirmed that a misalignment between images were adjusted after applying for the physical offset in the sensor model equation. An absolute position accuracy was analyzed in the Incheon dataset, compared to a ground control point. For the hyperspectral image, root mean square error (RMSE) for X, Y direction was calculated for 0.12 m, and for the point cloud, RMSE was calculated for 0.03 m. Furthermore, a relative position accuracy for a specific point between the adjusted point cloud and the hyperspectral images were also analyzed for 0.07 m, so we confirmed that a precise data mapping is available for an observation without a ground control point through the proposed estimation method, and we also confirmed a possibility of multi-sensor fusion. From this study, we expect that a flexible multi-sensor platform system can be operated through the independent parameter estimation method with an economic cost saving.