• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.103-106
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• 2003

In this paper, we present a hybrid inertial and vision-based tracking system for VR applications. One of the most important aspects of VR (Virtual Reality) is providing a correspondence between the physical and virtual world. As a result, accurate and real-time tracking of an object's position and orientation is a prerequisite for many applications in the Virtual Environments. Pure vision-based tracking has low jitter and high accuracy but cannot guarantee real-time pose recovery under all circumstances. Pure inertial tracking has high update rates and full 6DOF recovery but lacks long-term stability due to sensor noise. In order to overcome the individual drawbacks and to build better tracking system, we introduce the fusion of vision-based and inertial tracking. Sensor fusion makes the proposal tracking system robust, fast, accurate, and low jitter and noise. Hybrid tracking is implemented with Kalman Filter that operates in a predictor-corrector manner. Combining bluetooth serial communication module gives the system a full mobility and makes the system affordable, lightweight energy-efficient. and practical. Full 6DOF recovery and the full mobility of proposal system enable the user to interact with mobile device like PDA and provide the user with natural interface.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.11
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• pp.1117-1124
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• 2010

This paper presents a foot movement tracking system using ultrasonic sensors and inertial sensors, where the position and velocity of foot are computed using inertial sensors and ultrasonic sensors mounted on a shoe. A foot movement can be estimated using an inertial navigation algorithm only; however, the error tends to increase due to biases of gyroscopes and accelerometers. To reduce the error, a localization system using ultrasonic sensors is additionally used. In the localization system using ultrasonic sensors, the position is continuously calculated in the absolute coordinate. An indirect Kalman filter is used to combine inertial sensors and ultrasonic sensors. Through experiments, it is shown that the proposed system can track a foot movement.

• Journal of Biomedical Engineering Research
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• v.32 no.2
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• pp.118-126
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• 2011

This study proposes a human body orientation tracking system by inertial and earth magnetic sensors. These sensors were fused by indirect Kalman filter. The proposed tracking system was configured and the filter was implemented. The tracking performance was evaluated with static and dynamic tests. In static test, the sensor was fixed on the floor while its static characteristics was analyzed. In dynamic test, the sensor was held and moved manually for 30 seconds. The dynamic test included x, y, z axis rotations, and elbow flection/extension motions that mimic drinking. For these dynamic motions, the tracking angle error was under $4.1^{\circ}$ on average. The proposed tracking method is expected to be useful for various human body motion analysis.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.221-225
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• 1998

This paper is to present a method for recognizing an image of a tracking object by processing the image from a camera, whose attitude is controlled in inertial space with inertial co-ordinate system. In order to recognize an object, a pseudo-random M-array is attached on the object and it is observed by the camera which is controlled on inertial coordinate basis by inertial stabilization unit. When the attitude of the camera is changed, the observed image of M-array is transformed by use of affine transformation to the image in inertial coordinate system. Taking the cross-correlation function between the affine-transformed image and the original image, we can recognize the object. As parameters of the attitude of the camera, we used the azimuth angle of camera, which is de-fected by gyroscope of an inertial sensor, and elevation an91e of camera which is calculated from the gravitational acceleration detected by servo accelerometer.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1144-1148
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• 1996

This paper presents WMR modelling and path tracking algorithm using Inertial Navigation System. The error models of gyroscope and accelerometers in INS are derived by Gauss-Newton method which is nonlinear regression model. Then, to test availability of error model, we pursue the fitness diagnosis about probability characteristic for real data and estimated data. Performance of inertial sensor with error model and Kalman filter is pursued by comparing with one without them. The computer simulation shows that position error remarkably decrease when error compensation is applied.

• Journal of the Korea Institute of Military Science and Technology
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• v.5 no.3
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• pp.62-76
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• 2002

This paper describes the design of a Coordinates Tracking algorithm for EOTS and its error analysis. EOTS stabilizes the image sensors such as FLIR, CCD TV camera, LRF/LD, and so on, tracks targets automatically, and provides navigation capability for vehicles. The Coordinates Tracking algorithm calculates the azimuth and the elevation angle of EOTS using the inertial navigation system and the attitude sensors of the vehicle, so that LOS designates the target coordinates which is generated by a Radar or an operator. In the error analysis in this paper, the unexpected behaviors of EOTS that is due to the time delay and deadbeat of the digital signals of the vehicle equipments are anticipated and the countermeasures are suggested. This algorithm is verified and the error analysis is confirmed through simulations. The application of this algorithm to EOTS will improve the operational capability by reducing the time which is required to find the target and support especially the flight in a night time flight and the poor weather condition.

• 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 Institute of Control, Robotics and Systems
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• v.19 no.3
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• pp.190-194
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• 2013

This paper presents experimental results of the attitude control for a two-rotor system with 3-DOF(degree-of-freedom). Two DC motors are equipped at the two ends of a rectangular beam to generate lift force and the relation between motor voltage and lift force is found experimentally. And inertial sensors are mounted at the center of the beam to measure the roll angle and a complementary filter is designed to get the angle during DC motors driving. A controller with nonlinear compensation, integrator and state feedback to achieve asymptotic tracking for a step input and reject input disturbance is designed and experimented.

• Journal of the Korea Institute of Military Science and Technology
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• v.15 no.4
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• pp.374-380
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• 2012

In guided missile systems, reducing terminal-position error is the primary objective of the inertial navigation system. As a seeker is used to sense and track a target, the critical function of the inertial navigation system is to provide the seeker with accurate missile attitude information and help the seeker to keep tracking a target continuously. As inertial sensor body and missile body alignment errors are taken into account, it is desirable to minimize the alignment errors between the missile seeker and the attitude of inertial navigation system. Among the alignment errors, this paper addresses the methods of measuring and compensating misalignment between inertial sensor body and housing frame and shows test results of several experiments.