• Proceedings of KOSOMES biannual meeting
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• 2018.06a
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• pp.81-81
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• 2018

• Journal of Advanced Navigation Technology
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• v.27 no.1
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• pp.50-56
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• 2023

INS(Inertial Navigation System) calculates navigation information using a vehicle's acceleration and angular velocity without the outside information. However, when navigation is performed for a long time, navigation error gradually diverges and the performance decreases. To enhance INS's performance, the rotation of inertial measurement unit is developed to compensate error sources of inertial sensors, which is called RINS(Rotational Inertial Navigation System). This paper analyzes the influence of several errors for dual-axis RINS and the shows the results using simulation.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.10
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• pp.1002-1008
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• 2009

We consider a double faults detection and isolation problem using modified extended parity space approach for inertial measurement unit which use redundant inertial sensors. A redundant IMU which has a hardware redundant is composed of the cone shape because it is good for fault detection and isolation. We analyze the type of double faults and the reason why fault isolation performance is low. We propose modified extended parity space approach method using EPSA and the difference of sensor data.

• 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.

• Safety and Health at Work
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• v.3 no.3
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• pp.192-198
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• 2012

Objectives: Slip and fall accidents in the workplace are one of the top causes of work related fatalities and injuries. Previous studies have indicated that fall risk was related to postural and dynamic stability. However, the usage of this theoretical relationship was limited by laboratory based measuring instruments. The current study proposed a new method for stability assessment by use of inertial measurement units (IMUs). Methods: Accelerations at different body parts were recorded by the IMUs. Postural and local dynamic stability was assessed from these measures and compared with that computed from the traditional method. Results: The results demonstrated: 1) significant differences between fall prone and healthy groups in IMU assessed dynamic stability; and 2) better power of discrimination with multi stability index assessed by IMUs. Conclusion: The findings can be utilized in the design of a portable screening or monitoring tool for fall risk assessment in various industrial settings.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.355-359
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• 2006

For the poor observability of azimuth misalignment angle and east gyro drift rate of the traditional initial alignment, a bran-new SINS stationary fast self-alignment approach is proposed. By means of analyzing the characteristic of the strapdown inertial navigation system (SINS) stationary alignment seriously, the new approach takes full advantage of the specific force and angular velocity information given by inertial measurement unit (IMU) instead of the mechanization of SINS. Firstly, coarse alignment algorithm is presented. Secondly, a new fine alignment model for SINS stationary self-alignment is derived, and the observability of the model is analysed. Then, a modified Sage-Husa adaptive Kalman filter is introduced to estimate the misalignment angles. Finally, some computer simulation results illustrate the efficiency of the new approach and its advantages, such as higher alignment accuracy, shorter alignment time, more self-contained and less calculation.

• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.5
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• pp.623-629
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• 2018

The acceleration and the angular velocity that include natural frequencies of a missile detected by Inertial Measurement Unit(IMU) are transmitted to the control loop of a missile. The control loop command that is calculated using above signals can cause the resonance of the missile while it flies. Hence it is common to adapt the filter and the control loop for attenuating or eliminating the undesired signals such as natural frequencies. This paper introduces the new test technique using a floating system for performance evaluation of the designed filter and the control loop prior to a flight test. The proposed scheme can check out the degradation property of vibration in the filter and the control loop, while the conventional hardware-in-the-loop simulation(HILS) scheme cannot.

• Journal of Korea Spatial Information System Society
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• v.6 no.1 s.11
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• pp.51-58
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• 2004

Mobile Mapping System is an effective wav to obtain position and image using vehicle equipped with GPS(Global Positioning System), IMU(Inertial Measurement Unit), and CCD camera. It have been used various fields of load facility management, map upgrade and etc. It is difficult to upgrade Mobile Mopping System which is developed from abroad and add other sensors because we don't know the way to integrate and synchronize multi-sensors. In this paper, we present the effective way of the integration and synchronization method for multi sensors we designed and manufactured Synchronization equipment by considering sensors of laser, odometer and etc.

• The Journal of Korea Robotics Society
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• v.8 no.4
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• pp.283-291
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• 2013

Methods for measuring or estimating of ground shape by a laser range finder and a vision sensor(exteroceptive sensors) have critical weakness in terms that these methods need prior database built to distinguish acquired data as unique surface condition for driving. Also, ground information by exteroceptive sensors does not reflect the deflection of ground surface caused by the movement of UGVs. Thereby, UGVs have some difficulties regarding to finding optimal driving conditions for maximum maneuverability. Therefore, this paper proposes a method of recognizing exact and precise ground shape using Inertial Measurement Unit(IMU) as a proprioceptive sensor. In this paper, firstly this method recognizes attitude of a robot in real-time using IMU and compensates attitude data of a robot with angle errors through analysis of vehicle dynamics. This method is verified by outdoor driving experiments of a real mobile robot.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.9 no.4
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• pp.285-291
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• 2015

In this paper, we propose a classification algorithm for postures of sitting person by using IMU(inertial measurement unit). This algorithm uses PCA(principle component analysis) for decreasing the number of feature vectors to three and SVM(support vector machine) with RBF(radial basis function) kernel for classifying posture types. In order to collect the data, we designed neckband-shaped earphones with IMU, and applied it to three subjects who are healthy adults. Subjects were experimented three sitting postures, which are neutral posture, smartphoning, and writing. As the result, our PCA-SVM algorithm showed 95% confidence while the dimension of the feature vectors was reduced to 25%.