• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.10
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• pp.569-574
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• 2003

Computer vision system is broadly adapted like as autonomous vehicle system, product line inspection, etc., because it has merits which can deal with environment flexibly. However, for applying it for that industry, it has to clear the problem that recognize position parameter of itself. So that computer vision system stands in need of camera calibration to solve that. Camera calibration consists of the intrinsic parameter which describe electrical and optical characteristics and the extrinsic parameter which express the pose and the position of camera. And these parameters have to be reorganized as the environment changes. In traditional methods, however, camera calibration was achieved at off-line condition so that estimation of parameters is in need again. In this paper, we propose a method to the calibration of camera using line correspondence in image sequence varied environment. This method complements the corresponding errors of the point corresponding method statistically by the extraction of line. The line corresponding method is strong by varying environment. Experimental results show that the error of parameter estimated is within 1% and those is effective.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.11 no.3
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• pp.48-54
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• 2012

This paper introduces the kinematic calibration method to improve the positioning accuracy of a parallel mechanism. Since all the actuators in the parallel mechanism are controlled simultaneously toward the target position, the volumetric errors originated from each motion element are too complicated. Therefore, the exact evaluation of the error sources of each motion element and its calibration is very important in terms of volumetric errors. In the calibration processes, the measurement of the errors between commands and trajectories is necessary in advance. To do this, a digitizer was used for the data acquisition in 3 dimensional space rather than arbitrary planar error data. After that, the optimization process that was used for reducing the motion errors were followed. Consequently, Levenberg-Marquart algorithm as well as the error data acquisition method turned out effective for the purpose of the calibration of the parallel mechanism.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.247-250
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• 2008

The paper presents the advanced radiometric calibration method, called the lRCM (Iterative Radiometric Calibration Method), in order to avoid an operational constraint (solar source) for calibration. The IRCM assumes that an optical instrument is equipped with a filter assembly which consists of same band filters with different transmission ratios. Given all the noise sources (including the artificial one caused by the filters) of an image sensor, the noncentral ${\chi}^2$ distribution of the output result is induced by the approach of a noise PDF (Power Density Function). Finally, the radiometric calibration problem is transformed into equating two independent relations for the image sensor gains through the specified distribution.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.281-282
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• 2006

This paper proposed a method of the camera calibration for noncontact measure of badly illumination. The method with a semi-automation camera calibration, The user designates minimum 4 points in calibration pattern. The Perspective transformation predicts the remaining point calibration pattern. It uses at input value of the Tsai and it searches a better point, When being smaller allowable error than until, it repeats and it puts out a good calibration result.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.11
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• pp.1003-1007
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• 2015

Depth calibration is a procedure for finding the conversion function that maps disparity data from a depth-sensing camera to actual distance information. In this paper, we present an optimal depth calibration method for Kinect$^{TM}$ sensors based on an experimental design and convex optimization. The proposed method, which utilizes multiple measurements from only two points, suggests a simplified calibration procedure. The confidence ellipsoids obtained from a series of simulations confirm that a simpler procedure produces a more reliable calibration function.

• The Journal of Korea Robotics Society
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• v.17 no.2
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• pp.183-190
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• 2022

Accurate kinematic parameters of mobile robots are essential because inaccurate kinematic model produces considerable uncertainties on its odometry and control. Especially, kinematic parameters of caster type mobile robots are important due to their complex kinematic model. Despite the importance of accurate kinematic parameters for caster type mobile robots, few research dealt with the calibration of the kinematic model. Previous study proposed a calibration method that can only calibrate double-wheeled caster type mobile robot and requires direct-measuring of robot center point and distance between casters. This paper proposes a calibration method based on geometric approach that can calibrate single-wheeled caster type mobile robot with two or more casters, does not require direct-measuring, and can successfully acquire all kinematic parameters required for control and odometry. Simulation and hardware experiments conducted in this paper validates the proposed calibration method and shows its performance.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.10
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• pp.843-848
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• 2001

Most of gimballed inertial navigation systems(GNIS) are calibrated periodically to maintain their inherent accuracy. The existing calibration techniques using the conventional schuler test with the least square method and the multiposition test take a long time and have some problems in procedures. To solve this problem, calibration method using a linear Kalman filter is proposed by us. In this paper, the calibration method by the change of Schuler period is studied in order to improve the calibration performance of the gimballed INS. First of all, it is shown that the observability of Kalman filter is also enhanced the Schuler period is decreased. Simulation results show that the calibration performance using the present scheme is improved according to the decrease of the Schuler period and the calibration time is shortened extremely, too. And our proposed technique shows desirable estimation performance for the g-sensitive errors of inertial sensors in particular.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1568-1572
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• 2004

This paper proposes a new online calibration algorithm for the array antenna system. As you know, the several previous calibration methods for the mutual coupling did not estimate but measure mutual coupling effect at the real or test-bed system directly. Therefore we suggest some idea to compensate the calibration errors due to mutual coupling effect and mismatch in cables and electronic modules without the off-line calibration. In this work, we can calibrate the array antenna system under the operation of the system using Independent Component Analysis(ICA). This is what is called an online calibration. As you know, the ICA method has permutation and scaling problems. However, we solve problems of the ICA method and apply it to the calibration of an array antenna. The method simultaneously estimates the DOA(Direction of Arrival) of the signals, and calibrates the array for that specific angle. The proposed algorithm is evaluated by computer simulation and its behavior is illustrated by a numerical example.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.250-255
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• 2003

The position of a 3-dimensional(3D) point can be measured by using calibrated stereo camera. To obtain more accurate measurement ,more accurate camera calibration is required. There are many existing methods to calibrate camera. The simple linear methods are usually not accurate due to nonlinear lens distortion. The nonlinear methods are accurate more than linear method, but it increase computational cost and good initial guess is needed. The multi step methods need to know some camera parameters of used camera. Recent years, these explicit model based camera calibration work with the development of more precise camera models involving correction of lens distortion. But these explicit model based camera calibration have disadvantages. So implicit camera calibration methods have been derived. One of the popular implicit camera calibration method is to use neural network. In this paper, we propose implicit stereo camera calibration method for 3D reconstruction using support vector machine. SVM can learn the relationship between 3D coordinate and image coordinate, and it shows the robust property with the presence of noise and lens distortion, results of simulation are shown in section 4.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.7
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• pp.793-800
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• 2015

We presented a method to perform simultaneously both head-eye calibration and wheel calibration for a mobile robot that has a stereo camera mounted on the pan-tilt mechanism. Such a mobile robot system prevails recently. However, conventional methods are not applicable to this system because they assumed that camera systems were mounted on fixed structures. Building on conventional methods, we devised an iterative linear solution to solve the problem, and achieved satisfactory results in terms of accuracy in addition to efficiency due to simultaneous calibration. Furthermore, the calibration accuracy was improved by nonlinear optimization.