• 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.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.2
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• pp.455-458
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• 2006

관성항법장치는 관성항법장치를 구성하는 관성센서인 가속도계 및 자이로의 오차요소에 의해 수평축 항법오차는 슐러주기를 가지고 서서히 증가하는 반면에 수직축 오차는 기하급수적으로 증가하는 특성을 가지고 있다. 그러므로 관성항법장치를 장시간 운용하는 경우에는 비관성 보조센서를 이용하여 관성항법장치의 수직축 항법오차에 대한 보정을 반드시 수행하여야 한다. 관성항법장치의 수직축 항법오차를 보정하기 위한 비관성 보조센서의 일종인 기압고도계는 계측된 대기압과 모델링 된 대기압을 비교하여 항체의 고도를 측정하는 방법을 이용하기 때문에 항체의 자세변화 등에 매우 민감하고 대기압 측정오차에 의해 매우 큰 진폭의 잡음 및 바이어스가 존재한다. 본 논문에서는 시뮬레이션 및 시험을 통하여 기압 고도계의 잡음 및 바이어스 오차 성분에 의한 baro-inertial 고도루프의 성능분석 결과를 제시하고 기압고도계 잡음에 둔감한 INS/기압고도계 칼만필터의 설계 결과를 제시한다.

• Aerospace Engineering and Technology
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• v.9 no.1
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• pp.50-59
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• 2010

In this paper, the alignment and navigation results by INGU(Inertial Navigation and Guidance Unit) onboard software and by Inertial Explorer which is a post-processing software specialized for IMU(Inertial Measurement Unit) are compared for identification of inertial sensor error models and estimation of alignment and navigation errors for KSLV-I INGU. For verification of the IMU error estimated by Kalman Filter of Inertial Explorer, the covariance parameters of inertial sensor error model state are identified by using stochastic error model of inertial sensors estimated by Allan variance and the alignment and navigation test with static condition and the land navigation test with dynamic condition are carried out. The validity of inertial sensor model for KSLV-I INGU is verified by comparison the alignment and navigation results of INGU on-board software and Inertial Explorer.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.144-147
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• 2010

본 연구에서는 실내의 활동추적 시스템을 위해 가속도센서와 지자계 센서를 이용하여 외부로부터 독립적인 소형의 관성항법장치를 제안하였다. 기존의 실내 위치추적은 주로 GNSS(global navigation satellite system)의 정보를 가져와 실내 환경에 맞게 초음파와 RSSI(received signal strength indicator)등을 이용하여 구성한 경우가 연구되었으나 이러한 위성항법은 좌표 값이 미리 저장된 고정 노드가 필수적이라는 단점이 있다. 따라서 본 연구에서는 실내 환경과 같이 이동거리가 길지 않으며, 기존 환경 및 외부로부터의 영향에서 자유로운 관성항법을 이용한 실내 활동추적시스템을 제안하였다. 이를 위해 지자계 센서와 3축 가속도 센서를 사용한 신호 계측부와 Zigbee기반의 무선 센서 네트워크를 이용한 무선 전송부를 구성하였으며, 계측된 데이터의 분석으로부터 실내 위치추적의 가능성을 평가하였다.

• 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 Institute of Information and Commucation Sciences Conference
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• 2011.05a
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• pp.100-103
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• 2011

GFINS(gyro-free inertial navigation system)는 가속도 센서에서 계측된 데이터를 2차 적분하여 yaw를 계산한다. 하지만 가속도 센서는 외란과 적분 오차에 의한 누적오차가 지속적으로 커지는 문제가 있다. 따라서 본 논문에서는 퍼지 추론 시스템(FIS: fuzzy inference system)을 이용해 가속도 센서의 데이터를 보정함으로써 누적오차를 줄이는 방법을 제안한다. 제안된 방법의 성능평가를 위해, 직접 설계한 전방향 AGV를 이용하여 직선과 측면, 대각에 대해 반복 실험하였다. 실험 결과, 제안된 방법이 가속도 센서의 데이터를 효과적으로 보정하는 것을 확인하였다.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.4
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• pp.469-475
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• 2015

This paper describes about the method for designing an extended Kalman filter to accurately measure the position of the spatial-phase system using a semiconductor sensor. Spatial position is expressed by the correlation of the rotated coordinate system attached to the body from the inertia coordinate system (a fixed coordinate system). To express the attitude, quaternion was adapted as a state variable, Then, the state changes were estimated from the input value which was measured in the gyro sensor. The observed data is the value obtained from the acceleration sensor. By matching between the measured value in the acceleration sensor and the predicted calculation value, the best variable was obtained. To increase the accuracy of estimation, designation of the extended Kalman filter was performed, which showed excellent ability to adjust the estimation period relative to the sensor property. As a result, when a three-axis gyro sensor and a three-axis acceleration sensor were adapted in the estimator, the RMS(Root Mean Square) estimation error in simulation was retained less than 1.7[$^{\circ}$], and the estimator displayed good property on the prediction of the state in 100 ms measurement period.

• Journal of Ocean Engineering and Technology
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• v.19 no.5 s.66
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• pp.71-77
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• 2005

This paper presents an integrated underwater navigational algorithm for unmanned underwater vehicles, using additional two-range transducers. This paper proposes a measurement model, using two range measurements, to improve the performance of an IMU-DVL (inertial measurement unit - Doppler velocity log) navigation system for long-time operation of underwater vehicles, excluding DVL measurement. Extended Kalman filter was adopted to propagate the error covariance, to update the measurement errors, and to correct the state equation when the external measurements are available. Simulation was conducted with the 6-d.o.f nonlinear numerical model of an AUV in lawn-mowing survey mode, at current flaw, where the velocity information is unavailable. Simulations illustrate the effectiveness of the integrated navigation system, assisted by the additional range measurements without DVL sensing.

• Proceedings of the KIPE Conference
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• 2006.06a
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• pp.13-15
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• 2006

센서리스 제어를 위한 상태 관측기의 설계시 기존의 바이너리 관측기의 성능을 향상시키기 위해 스위칭 평면에 적분항을 추가함으로서 정상상태의 성능을 향상시킬 수 있다. 또한 전동기의 관성계수나 마찰계수와 같은 운동방정식의 파라미터들은 계측하기 어려우며 관측기의 성능저하를 야기하므로 실제구현에서도 많은 제약이 따른다. 이러한 문제를 해결하기 위해 적응 적분바이리 관측기를 제안하다. 그리고 정격속도 이상으로 구동하기 위해서 약계자 영역에서의 센서리스 제안한다. IPMSM이 w가 보다 높은 속도에서 운전되기 위해서는 정격전압이상의 입력이 요구되어진다. 그러나 정격전압에서 d축 전류가 회전자 자속을 감소시키는 방향으로 주입된다면 더 높은 속도에서 운전되어질 수 있다.

• Journal of the Korean Society for Precision Engineering
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• v.32 no.4
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• pp.377-386
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• 2015

Joint force/torque estimation by inverse dynamics is a traditional tool in biomechanical studies. Conventionally for this, kinematic data of human body is obtained by motion capture cameras, of which the bulkiness and occlusion problem make it hard to capture a broad range of movement. As an alternative, inertial motion sensing using cheap and small inertial sensors has been studied recently. In this research, the performance of inertial motion sensing especially to calculate inverse dynamics is studied. Kinematic data from inertial motion sensors is used to calculate ground reaction force (GRF), which is compared to the force plate readings (ground truth) and additionally to the estimation result from optical method. The GRF estimation result showed high correlation and low normalized RMSE(R=0.93, normalized RMSE<0.02 of body weight), which performed even better than conventional optical method. This result guarantees enough accuracy of inertial motion sensing to be used in inverse dynamics analysis.