• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.171-174
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• 2003

In this paper, we propose the method of stereo images composition using adaptive dense disparity estimation. For the correct composition of stereo image and 3D virtual object, we need correct marker position and depth information. The existing algorithms use position information of markers in stereo images for calculating depth of calibration object. But this depth information may be wrong in case of inaccurate marker tracking. Moreover in occlusion region, we can't know depth of 3D object, so we can't composite stereo images and 3D virtual object. In these reasons, the proposed algorithm uses adaptive dense disparity estimation for calculation of depth. The adaptive dense disparity estimation is the algorithm that use pixel-based disparity estimation and the search range is limited around calibration object.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.11
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• pp.884-890
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• 2018

In this paper, an improved method of estimating static source location is proposed based on the direct position determination(DPD) method, which estimates a source position directly using received signals. When the source position is estimated using the conventional DPD method, the estimation accuracy and error depend on a pair of receivers: a reference receiver and one of the multiple moving receivers. Based on this, the weighting values of the estimating source location were obtained using the covariance matrix for the pair of receivers($S_1$, $S_{2i}$) and applied to the DPD algorithm. Finally, the source position was estimated using the proposed DPD algorithm, and it was verified that the estimation accuracy improved, compared to the conventional DPD algorithm.

• Journal of KIISE
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• v.43 no.12
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• pp.1315-1324
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• 2016

Recently, the increasing interest in drones has resulted in development of new related technologies. Attention has been focused toward research on swarm flight which controls drones simultaneously without collision. Thus, complicated missions can be completed rapidly through collaboration between drones. Due to low position accuracy, GPS is not appropriate for the outdoor mission involving accurate flight. In addition, the inaccurate position estimation of GPS gives rise to the serious problem of collision, since many drones are controlled in a narrow space. In this study, we increased the accuracy of position estimation through various sensors with Real-Time Kinematic-GPS (RTK-GPS). The mode switching algorithm was proposed to minimize the problem of sensor error. In addition, we introduced the outdoor swarm flight system based on the proposed position estimation.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.541-546
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• 2005

Tracking control of an automatic pipe cutting robot (APCROMB) is studied. Using magnetic force APCROMB, which is designed and developed in Kyung Hee University, binds itself to the pipe and executes unmanned cutting process. The gravity effect on the movement of APCROMB varies as it rotates around the cylindrical pipe laid in the gravitational field. To maintain a constant velocity and consistent cutting performance against the varying gravitational effect, the authors adopt a multi-rate repetitive learning controller (MRLC), which learns the required effort to cancel the repetitive tracking errors caused by nonlinear effect. In addition to the varying gravity effect other types of nonlinear disturbances including backlash in the driving system and the slip between the wheels of APCROMB and the pipe also cause degradation in the cutting process. In order to identify those nonlinear disturbances the position estimation based on the encoder attached at the motor is not good enough. To identify the absolute angular position of APCROMB the authors propose the angular position estimation based on the signals from a MEMS-type two-axis accelerometer mounted on APCROMB. The tracking performances of APCROMB with a MRLC using the encoder-based position estimation is experimentally measured and results are shown. Also the difference between the encoder-based angular displacement measurement and the accelerometerbased angular displacement measurement is included.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.26 no.1
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• pp.89-96
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• 2016

TDOA(time delay of arrival) position estimate from acoustic measurement of artillery shell impact is studied in order to develop a targeting evaluation system. Impact position is calculated from the intersections of hyperbolic estimates based on the least square Taylor series method. The mathematical process of Taylor series estimation is known to be robust. However, the concern lays with the accuracy because it is sensitive to the bias caused by the randomness of measurement situation. The measurement error typically occurs from the distortion of waveform, change of travelling path, and sensor position error. For outdoor measurement, a consideration should be made on the atmospheric condition such as temperature and wind which can possibly change the trajectories of rays of sound. It produces wrong propagation time events accordingly. Ray tracing and optimization techniques are introduced in this study to minimize the bias induced by the ray of sound. The numerical simulation shows that the atmospheric correction improves the estimation accuracy.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.2
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• pp.164-172
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• 2008

This paper presents the efficient localization of a mobile robot traveling on the floor with tags installed, using the spatial and temporal information acquired from passive RFID environment. Compared to previous research, the proposed localization method can reduce the position estimation error and also cut down the initial cost tag installation cost. Basically, it is assumed that a mobile robot is traveling over a series of straight line segments, each at a certain constant velocity, and that the number of tags sensed by a mobile robot at each sampling instant is at most one. First, the velocity and position estimation of a mobile robot starting from a known position, which is valid for all segments except the first one. Second, for the first segment in which the starting position is unknown, the velocity and position estimation is made possible by enforcing a mobile robot to traverse at least two tags at a constant velocity with the steering angle unchanged. Third, through experiments using our passive RFID localization system, the validity and performance of the mobile robot localization proposed in this paper is demonstrated.

• Journal of Sensor Science and Technology
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• v.31 no.3
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• pp.156-162
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• 2022

This study estimates the relative position between body segments using segment orientation and segment-to-joint center (S2J) vectors. In many wearable motion tracking technologies, the S2J vector is treated as a constant based on the assumption that rigid body segments are connected by a mechanical ball joint. However, human body segments are deformable non-rigid bodies, and they are connected via ligaments and tendons; therefore, the S2J vector should be determined as a time-varying vector, instead of a constant. In this regard, our previous study (2021) proposed a method for determining the time-varying S2J vector from the learning dataset using a regression method. Because that method uses a deformation-related variable to consider the deformation of S2J vectors, the optimal variable must be determined in terms of estimation accuracy by motion and segment. In this study, we investigated the effects of deformation-related variables on the estimation accuracy of the relative position. The experimental results showed that the estimation accuracy was the highest when the flexion and adduction angles of the shoulder and the flexion angles of the shoulder and elbow were selected as deformation-related variables for the sternum-to-upper arm and upper arm-to-forearm, respectively. Furthermore, the case with multiple deformation-related variables was superior by an average of 2.19 mm compared to the case with a single variable.

• The Journal of the Acoustical Society of Korea
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• v.38 no.6
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• pp.630-636
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• 2019

Passive Ranging Sonar (PRS) is a type of passive sonar consisting of three sub-array on the port and starboard, and has a characteristic of detecting a target and calculating a bearing and a distance. The bearing and distance calculation requires physical sub-array position information, and the bearing and distance accuracy performance are deteriorated when the position information of the sub-array is inaccurate. In particular, it has a greater impact on distance accuracy performance using plus value of two time-delay than a bearing using average value of two time-delay. In order to improve this, a study on sub-array position error estimation and error compensation is needed. In this paper, We estimate the sub-array position error based on enetic algorithm, an optimization search technique, and propose a method to improve the performance of distance accuracy by compensating the time delay error caused by the position error. In addition, we will verify the proposed algorithm and its performance using the sea-going data.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.5
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• pp.398-405
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• 2005

The intelligent robots that will be needed in the near future are human-friendly robots that are able to coexist with humans and support humans effectively. To realize this, robots need to recognize their position and posture in known environment as well as unknown environment. Moreover, it is necessary for their localization to occur naturally. It is desirable for a robot to estimate of his position by solving uncertainty for mobile robot navigation, as one of the best important problems. In this paper, we describe a method for the localization of a mobile robot using image information of a moving object. This method combines the observed position from dead-reckoning sensors and the estimated position from the images captured by a fixed camera to localize a mobile robot. Using a priori known path of a moving object in the world coordinates and a perspective camera model, we derive the geometric constraint equations which represent the relation between image frame coordinates for a moving object and the estimated robot's position. Also, the control method is proposed to estimate position and direction between the walking human and the mobile robot, and the Kalman filter scheme is used for the estimation of the mobile robot localization. And its performance is verified by the computer simulation and the experiment.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.5
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• pp.453-463
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• 2001

This paper presents an absolute position estimation method using Indian remote sensing (IRS) satellite images, which is a part of a position estimation (PE) system. The accumulated buffer (AB) matching method is proposed, in which a set of accumulator cells is employed for fast edge-based matching. By computer simulations with two sets of veal aerial image sequences, the performance of the AB matching method is analyzed and its effectiveness is shown in terms of the position error in the hybrid PE system.