• 한국항해항만학회지
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• 제35권10호
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• pp.785-791
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• 2011

본 논문에서는 소형 선박용 관성측정장치(Inertial Measurement Unit, IMU) 개발에 적합한 MEMS(Micro-Electro Mechanical System) 기반의 관성 센서 평가와 선정에 관하여 기술했다. 먼저, 오일러 공식에 기초한 관성 센서의 오차 모델과 잡음 모델을 정의하고, 앨런 분산(Allan Variance) 기법과 몬테카르로(Monte Carlo) 시뮬레이션 기법을 도입하여 관성 센서를 평가하였다. ADIS16405, SAR10Z, SAR100Grade100, LIS344ALH, ADXL103 등 다섯 가지 관성 센서에 대한 평가결과, ADIS16405의 자이로와 가속도계를 조합한 경우 오차가 가장 작게 나타났는데, 600 초 경과시 속도 오차의 표준편차가 약 160 m/s, 위치 오차의 표준편차가 약 35 km로 나타났다. 평가를 통해 ADIS16405 관성 센서가 IMU 구축에 최적임을 알았고, 이러한 오차 감소 방법에 대해서 참고문헌을 조사하여 검토하였다.

• 한국해양공학회지
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• 제17권6호
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• pp.83-90
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• 2003

This paper presents an underwater hybrid navigation system for a semi-autonomous underwater vehicle (SAUV). The navigation system consists of an inertial measurement unit (IMU), and a Doppler velocity log (DVL), accompanied by a magnetic compass. The errors of inertial measurement units increase with time, due to the bias errors of gyros and accelerometers. A navigational system model is derived, to include the scale effect and bias errors of the DVL, of which the state equation composed of the navigation states and sensor parameters is 20. The conventional extended Kalman filter was used to propagate the error covariance, update the measurement errors, and correct the state equation when the measurements are available. Simulation was performed with the 6-d.o,f equations of motion of SAUV, using a lawn-mowing survey mode. The hybrid underwater navigation system shows good tracking performance, by updating the error covariance and correcting the system's states with the measurement errors from a DVL, a magnetic compass, and a depth sensor. The error of the estimated position still slowly drifts in the horizontal plane, about 3.5m for 500 seconds, which could be eliminated with the help of additional USBL information.

• 실천공학교육논문지
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• 제10권2호
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• pp.119-124
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• 2018

IMU(Inertial Measurement Unit) 센서의 가속도와 각속도를 이용하여 거리측정을 하고 측정값을 이용하여 사용자가 원하는 실내공간에 적용하여 사용자 혹은 디바이스가 실내공간을 인식하는 교육용 장비를 소개한다. 본 교육장비를 이용해서 다양한 위치 인식 및 추적 알고리즘을 학습할 수 있고 창의적 공학설계 작품을 구현할 수 있다. IMU 센서의 데이터 값을 $I^2C$(Inter-Integrated Circuit)을 통해 MCU(microcontroller unit)에 전송하고 필터와 연산방식을 통해 데이터 값을 처리 후 실내 위치 인식 알고리즘을 통해 위치인식을 한다. 그리고 무선통신을 이용하여 처리된 값을 송수신하여 사용자가 인식하도록 설계한다. 본 교육 장비를 이용하여 "IMU센서를 이용하여 이동거리를 산출과 데이터 값을 이용한 가상공간 구현 및 인식"의 사례를 소개하고 그 설계를 기반하여 다양한 창의적 공학설계 적용에 대해서 논한다.

• 한국해양공학회:학술대회논문집
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• 한국해양공학회 2003년도 춘계학술대회 논문집
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• pp.149-156
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• 2003

This paper presents an underwater hybrid navigation system for a semi-autonomous underwater vehicle (SAUV). The navigation system consists of an inertial measurement unit (IMU), an ultra-short baseline (USBL) acoustic navigation sensor and a doppler velocity log (DVL) accompanying a magnetic compass. The errors of inertial measurement units increase with time due to the bias errors of gyros and accelerometers. A navigational system model is derived to include the error model of the USBL acoustic navigation sensor and the scale effect and bias errors of the DVL, of which the state equation composed of the navigation states and sensor parameters is 25 in the order. The conventional extended Kalman filter was used to propagate the error covariance, update the measurement errors and correct the state equation when the measurements are available. Simulation was performed with the 6-d.o.f. equations of motion of SAUV in a lawn-mowing survey mode. The hybrid underwater navigation system shows good tracking performance by updating the error covariance and correcting the system's states with the measurement errors from a DVL, a magnetic compass and a depth senor. The error of the estimated position still slowly drifts in horizontal plane about 3.5m for 500 seconds, which could be eliminated with the help of additional USBL information.

• 항공우주기술
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• 제9권1호
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• pp.50-59
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• 2010

본 논문에서는 KSLV-I 관성항법유도장치 관성계측부에 대한 오차 모델 확인 및 항법오차 추정을 위해 관성항법유도장치 탑재 소프트웨어를 통한 정렬 및 항법계산 결과와 관성계측유닛 후처리 소프트웨어인 Inertial Explorer를 통한 정렬 및 항법계산 결과를 비교하였다. Inertial Explorer의 칼만필터를 통한 관성계측부 오차 추정 정확도 확인을 위해 Allan Variance를 통한 관성계측부 확률적 오차모델을 이용하여 관성계측부 오차모델 상태변수 공분산 값을 설정하였고, 정적상태에서의 정렬 및 항법시험, 동적환경에서의 주행항법시험을 수행하였다. INGU 탑재 소프트웨어와 Inertial Explorer를 통한 정렬 및 항법계산 결과 비교를 통해 본 논문에 설정한 KSLV-I 관성항법유도장치 관성센서 오차모델의 유효성을 확인하였다.

• International Journal of Aeronautical and Space Sciences
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• 제6권1호
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• pp.27-43
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• 2005

An INS(Inertial Navigation System) is composed of a navigation computer and an IMU(Inertial Measurement Unit), and can be applied to estimate a vehicle's state. But the inertial sensors assembled in the IMU are too complicated and expensive to use for the general application purpose. In this study, a new concept of inertial sensor system using magnetic levitation is proposed. The proposed system is expected to replace one single-axis rate or position gyroscope, and one single-axis accelerometer concurrently with a relatively simple structure. A simulation of the proposed system is given to describe the capability of this new concept.

• International Journal of Aeronautical and Space Sciences
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• 제11권3호
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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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권8호
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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.

• 제어로봇시스템학회논문지
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• 제16권12호
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• pp.1167-1175
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• 2010

This paper presents the use of the inertial measurement unit information and the infrared sector information for getting the position of an object. Travel distance is usually calculated from the double integration of the accelerometer output with respect to time; however, the accumulated errors due to the drift are inevitable. The orientation change of the accelerometer also causes error because the gravity is added to the measured acceleration. Unless three axis orientations are completely identified, the accelerometer alone does not provide correct acceleration for estimating the travel distance. We propose a way of minimizing the error due to the change of the orientation. In order to reduce the accumulated error, the infrared sector information is fused with the inertial measurement unit information. Infrared sector information has highly deterministic characteristics, different from RFID. By putting several infrared emitters on the ceiling, the floor is divided into many different sectors and each sector is set to have a unique identification. Infrared light based sector information tells the sector the object is in, but the size of the uncertainty is too large if only the sector information is used. This paper presents an algorithm which combines both the inertial measurement unit information and the sector information so that the size of the uncertainty becomes smaller. It also introduces a framework which can be used with other types of the artificial landmarks. The characteristics of the developed infrared light based sector and the proposed algorithm are verified from the experiments.

• 한국전문물리치료학회지
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• 제30권2호
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• pp.102-109
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• 2023

Background: While efforts have been made to differentiate fall risk in older adults using wearable devices and clinical methodologies, technologies are still infancy. We applied a decision tree (DT) algorithm using inertial measurement unit (IMU) sensor data and clinical measurements to generate high performance classification models of fall risk of older adults. Objects: This study aims to develop a classification model of fall risk using IMU data and clinical measurements in older adults. Methods: Twenty-six older adults were assessed and categorized into high and low fall risk groups. IMU sensor data were obtained while walking from each group, and features were extracted to be used for a DT algorithm with the Gini index (DT1) and the Entropy index (DT2), which generated classification models to differentiate high and low fall risk groups. Model's performance was compared and presented with accuracy, sensitivity, and specificity. Results: Accuracy, sensitivity and specificity were 77.8%, 80.0%, and 66.7%, respectively, for DT1; and 72.2%, 91.7%, and 33.3%, respectively, for DT2. Conclusion: Our results suggest that the fall risk classification using IMU sensor data obtained during gait has potentials to be developed for practical use. Different machine learning techniques involving larger data set should be warranted for future research and development.