• 한국항해항만학회지
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• 제28권9호
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• pp.803-808
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• 2004

In this paper, an INS compensation algorithm is proposed using the accelerometer from IMU. First, we denote the basic INS algorithm and show that how to compensate the position error when low cost IMU is used. Second, considering the ship's characteristic and ocean environments, we consider with a drift as a periodic external environment change which is affected with exact position. To develop the compensation algorithm, we use a repetitive method to reduce the external environment changes. Lastly, we verify the proposed algorithm through the experiments, where the acceleration sensor is used to acquire real data.

• 한국측량학회:학술대회논문집
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• 한국측량학회 2004년도 추계학술발표회 논문집
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• pp.117-121
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• 2004

In this paper, the error compensation method of the low-cost IMU is proposed. In general, the position and attitude error calculated by accelerometers and gyros grows with time. Therefore the additional information is required to compensate the drift. The attitude angles can be bound accelerometer mixing algorithm and the heading angle can be aided by single antenna GPS velocity. The Kalman filter is used for error compensation. The result is verified by comparing with the attitude calculated and dynamics position determination by Attitude Heading Reference System with Micro Electro Mechanical System for a basis

• International Journal of Ocean System Engineering
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• 제1권3호
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• pp.165-170
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• 2011

In this paper, for the accurate estimation of the position and orientation of the UUV (unmanned underwater vehicle), an AHRS (Attitude Heading Reference System) was developed using the IMU (inertial measurement unit) sensor which provides information on acceleration and orientation in the object coordinate and the initial alignment algorithm and the E-KF (extended Kalman Filter). The initial position and orientation of the UUV are estimated using the initial alignment algorithm with 3-axis acceleration and geomagnetic information of the IMU sensor. The position and orientation of the UUV are estimated using the AHRS composed of 3-axis acceleration, velocity, and geomagnetic information and the E-KF. For the performance test of the orientation estimation of the AHRS, a testbed using IMU sensor(ADIS16405) and DSP28335 coded with an E-KF algorithm was developed and its performance was verified through tests.

• 대한공간정보학회지
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• 제10권3호
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• pp.89-97
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• 2002

45(GNSS, SIIS, GIS, ITS) 기술의 핵심이 되는 공간정보의 상호 공유 극대화를 위하여 원격지 공간 데이터 공유 및 제공을 위하여 45-Van 시스템을 개발해오고 있다. 45-Van 시스템은 GPS/IMU, 레이저, CCD 영상, 무선통신기술을 통합 연계하여 현장에서 GIS용 DB 정보 등과 같은 45 핵심 DB정보 및 정확한 위치 정보를 직접 획득 생산이 가능하다. 즉, 4S-Van은 GPS와 IMU의 통합으로 카메라의 위치 및 자세를 결정하며, 두 대의 CCD카메라로 전방을 촬영하여, 공간전방 교회법(Space Intersection)으로 피사체의 위치해석을 하게 되고 기존의 벡터 DB 체계와 호환됨으로써 데이터베이스의 구축 및 현장활용이 가능하도록 할 수 있는 기술이다. 또한 적외선 카메라 및 무선 통신 기술을 활용한 다양한 응용이 가능하다. 본 논문에서는 GPS, CCD 카메라, IMU의 차량 탑재에 의한 45-Van 설계와 기능에 대하여 살펴본다.

• 대한조선학회논문집
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• 제61권2호
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• pp.98-105
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• 2024

Due to the low accuracy of measured data obtained from low-cost GNSS and IMU devices, it was hard to secure the required accuracy of the measured position and heading angle for autonomous navigation which was conducted by a model-scale marine mobility. In this paper, a localization technique using the Extended Kalman Filter (EKF) is proposed for coping with the issue. First of all, a position and heading angle estimator is developed using EKF with the assumption of a point mass model. Second, the measured data from GNSS and IMU, including position, heading angle, and velocity are used for the estimator. In addition, the heading angle is additionally obtained by comparing the LiDAR point cloud with map information for a temporal water tank. The newly acquired heading angle is integrated into the estimator as an additional measurement to correct the inaccuracy in the heading angle measured from the IMU. The effectiveness of the proposed approach is investigated using data acquired from preliminary tests of the model-scale autonomous marine mobility.

• 전기학회논문지
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• 제60권12호
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• pp.2339-2345
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• 2011

This paper proposes a new multi-filtered inertial navigation system to estimate the attitude and position of moving objects. This system has two states, the one is attitude state and the other is position/velocity state. For compensating IMU sensor errors, each of the two states uses a different filter: the attitude state uses the EKF and the position state uses the UPF. The fast and precise characteristics of the EKF have been properly utilized for the attitude estimation, while superior dynamic characteristics of the UPF have been fully adopted for the position estimation. The combination of these two filters in an inertial navigation system improves the system performance to be faster and more accurate. Experimental results demonstrate the superiority of this approach comparing to the conventional ones.

• 제어로봇시스템학회논문지
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• 제18권8호
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• pp.744-749
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• 2012

In this paper, we suggest that how to improve an accuracy of mobile robot's localization by using the sensor network information which fuses the machine vision camera, encoder and IMU sensor. The heading value of IMU sensor is measured using terrestrial magnetism sensor which is based on magnetic field. However, this sensor is constantly affected by its surrounding environment. So, we isolated template of ceiling using vision camera to increase the sensor's accuracy when we use IMU sensor; we measured the angles by pattern matching algorithm; and to calibrate IMU sensor, we compared the obtained values with IMU sensor values and the offset value. The values that were used to obtain information on the robot's position which were of Encoder, IMU sensor, angle sensor of vision camera are transferred to the Host PC by wireless network. Then, the Host PC estimates the location of robot using all these values. As a result, we were able to get more accurate information on estimated positions than when using IMU sensor calibration solely.

• 제어로봇시스템학회논문지
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• 제19권11호
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• pp.1053-1059
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• 2013

Heading bias effects in PNS using IMUs attached to shoes are analyzed in this paper. The navigation algorithms of a single foot PNS where one IMU is attached to a foot and dual foot PNSs where two IMUs are attached to each foot are derived. Two navigation algorithms are proposed for the dual foot PNS: 1) the positions from the independent right and left foot PNSs are averaged to provide the final position, 2) the right and left foot PNSs are correlated and it provides positions of each foot. Furthermore, it is proven that two methods are equal. Using the derived navigation algorithms the effect of heading bias caused by a misalignment of the moving direction and IMU is analyzed. The analysis explains the position error of a single foot PNS is diverged while the heading bias is effectively compensated in dual foot PNSs because of the symmetry of heading biases. The experimental results confirm the analysis.

• 한국측량학회지
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• 제28권1호
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• pp.65-72
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• 2010

본 연구에서는 MMS인 경우 고성능의 중급 IMU가 사용되고 보행자 항법시스템에서는 MEMS형의 저급 IMU가 사용된다고 가정한 후 GPS 신호가 단절되었을 경우 IMU에 의해 생성되는 위치 및 자세 오차를 시뮬레이션을 통하여 계산하였다. 또한 GPS 신호 단절 시에 고도계, 전자나침반 및 2가지 센서를 동시에 이용하는 MultiSensor를 이용하여 중급 및 저급 IMU를 보정하였을 경우의 정확도 향상 효과를 분석하였다. 실험 결과 중급 IMU의 경우 MMS에서 요구되는 3차원 위치오차 정확도가 5m라고 가정할 경우 GPS 단절 시간이 30초가 넘으면 요구 정확도를 만족하지 못하였다. 하지만 GPS 단절 구간에서 고도계 전자나침반 그리고 MultiSensor를 이용하여 IMU 보정을 수행할 경우 약 60초까지 요구정확도를 만족하였다. 또한 고도계 및 전자나침반을 동시에 사용할 경우 고도계에 의한 영향이 더욱 큰 것으로 판단된다 MEMS IMU와 같은 저급 IMU가 사용되는 보행자 항법 시스템의 요구 위치 정확도가 약 20m라고 가정할 경우 4초 이후에는 요구 정확도를 만족하지 못하였으며 자세 오차도 매우 급증하였다. 하지만 GPS 신호 단절시 보조센서를 이용하여 저급 IMU 보정을 수행하였을 경우 약 15초까지 요구 정확도를 만족할수 있을 것으로 시뮬레이션 결과 예측되었으며 또한 중급 IMU 실험과는 반대로 보행자 항법과 같은 속도가 느린 시스템에서 고도계 및 전자나침반 2가지 센서를 동시에 사용할 경우 전자나침반에 의한 영향이 더욱 큰 것으로 나타났다. 본 연구는 GPS 신호 단절이 발생할 수 있는 지역에 대하여 MMS 또는 보행자 항법시스템을 운용할 경우 요구 정확도에 따른 보조센서 통합을 이용하여 정확도를 높이는 자료로써 사용될 수 있을 것으로 예상된다.

• 제어로봇시스템학회논문지
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• 제19권6호
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• pp.569-575
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• 2013

This paper proposes a method for PDR (Pedestrian Dead-Reckoning) using a low cost IMU. Generally, GPS has been widely used for localization of pedestrians. However, GPS is disabled in the indoor environment such as in buildings. To solve this problem, this research suggests the PDR scheme with an IMU attached to the pedestrian's waist. However, despite the fact many methods have been proposed to estimate the pedestrian's position, but their results are not sufficient. One of the most important factors to improve performance is, a new calibration method that has been proposed to obtain the reliable sensor data. In addition to this calibration, the PDR method is also proposed to detect steps, where estimation schemes of step length, attitude, and heading angles are developed. Peak and zero crossings are detected to count the steps from 3-axis acceleration values. For the estimation of step length, a nonlinear step model is adopted to take advantage of using one parameter. Complementary filter and zero angular velocity are utilized to estimate the attitude of the IMU module and to minimize the heading angle drift. To verify the effectiveness of this scheme, a real-time system is implemented and demonstrated. Experimental results show an accuracy of below 1% and below 3% in distance and position errors, respectively, which can be achievable using a high cost IMU.