• 한국정밀공학회지
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• 제22권9호
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• pp.77-84
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• 2005

In most vision applications, we are frequently confronted with determining the position of object continuously. Generally, intertwined processes ire needed for target tracking, composed with tracking and control process. Each of these processes can be studied independently. In case of actual implementation we must consider the interaction between them to achieve robust performance. In this paper, the robust real time visual tracking in complex background is considered. A common approach to increase robustness of a tracking system is to use known geometric models (CAD model etc.) or to attach the marker. In case an object has arbitrary shape or it is difficult to attach the marker to object, we present a method to track the target easily as we set up the color and shape for a part of object previously. Robust detection can be achieved by integrating voting-based visual cues. Kalman filter is used to estimate the motion of moving object in 3D space, and this algorithm is tested in a pan/tilt robot system. Experimental results show that fusion of cues and motion estimation in a tracking system has a robust performance.

• 한국생산제조학회지
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• 제23권5호
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• pp.485-497
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• 2014

This paper presents the robot vision control schemes based on the Newton-Raphson (N-R) and the Extended Kalman Filter (EKF) methods for the tracking of moving targets. The vision system model used in this study involves the six camera parameters. The difference is that refers to the uncertainty of the camera's orientation and focal length, and refers to the unknown relative position between the camera and the robot. Both N-R and EKF methods are employed towards the estimation of the six camera parameters. Based on the these six parameters estimated using three cameras, the robot's joint angles are computed with respect to the moving targets, using both N-R and EKF methods. The two robot vision control schemes are tested by tracking the moving target experimentally. Given the experimental results, the two robot control schemes are compared in order to evaluate their strengths and weaknesses.

• 한국멀티미디어학회논문지
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• 제10권6호
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• pp.716-725
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• 2007

In this paper we describe a motion tracking algorithm for 3D human animation using stereo vision system. This allows us to extract the motion data of the end effectors of human body by following the movement through segmentation process in HIS or RGB color model, and then blob analysis is used to detect robust shape. When two hands or two foots are crossed at any position and become disjointed, an adaptive algorithm is presented to recognize whether it is left or right one. And the real motion is the 3-D coordinate motion. A mono image data is a data of 2D coordinate. This data doesn't acquire distance from a camera. By stereo vision like human vision, we can acquire a data of 3D motion such as left, right motion from bottom and distance of objects from camera. This requests a depth value including x axis and y axis coordinate in mono image for transforming 3D coordinate. This depth value(z axis) is calculated by disparity of stereo vision by using only end-effectors of images. The position of the inner joints is calculated and 3D character can be visualized using inverse kinematics.

• 제어로봇시스템학회논문지
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• 제19권7호
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• pp.599-604
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• 2013

Path planning of mobile robots has a purpose to design an optimal path from an initial position to a target point. Minimum driving time, minimum driving distance and minimum driving error might be considered in choosing the optimal path and are correlated to each other. In this paper, an efficient driving trajectory is planned in a real situation where a mobile robot follows a moving object. Position and distance of the moving object are obtained using a web camera, and the rotation angular and linear velocities are estimated using Kalman filters to predict the trajectory of the moving object. Finally, the mobile robot follows the moving object using a single curvature trajectory by estimating the trajectory of the moving object. Using the estimation by Kalman filters and the single curvature in the trajectory planning, the total tracking distance and time saved amounts to about 7%. The effectiveness of the proposed algorithm has been verified through real tracking experiments.

• International Journal of Precision Engineering and Manufacturing
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• 제5권1호
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• pp.27-35
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• 2004

In the field of machine vision using a single camera mounted on a mobile robot, although the detection and tracking of moving objects from a moving observer, is complex and computationally demanding task. In this paper, we propose a new scheme for a mobile robot to track and capture a moving object using images of a camera. The system consists of the following modules: data acquisition, feature extraction and visual tracking, and trajectory generation. And a single camera is used as visual sensors to capture image sequences of a moving object. The moving object is assumed to be a point-object and projected onto an image plane to form a geometrical constraint equation that provides position data of the object based on the kinematics of the active camera. Uncertainties in the position estimation caused by the point-object assumption are compensated using the Kalman filter. To generate the shortest time trajectory to capture the moving object, the linear and angular velocities are estimated and utilized. The experimental results of tracking and capturing of the target object with the mobile robot are presented.

• 한국항해항만학회지
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• 제37권5호
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• pp.447-452
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• 2013

해상교통관제시스템(VTS)은 선박의 위치, 속도, 침로 등 해상 교통 정보를 획득하기 위하여 다수의 해상감시레이더를 주요 센서로 이용하고 있으며, 거리 및 방위각 바이어스와 같은 2차원 해상감시레이더의 시스템 오차는 레이더 영상 및 표적 추적정보의 정확도를 크게 저하시킬 수 있다. 따라서 해상교통관제시스템에서 정확한 표적정보를 제공하기 위하여 레이더의 시스템 오차는 정밀하게 보정되어야 한다. 본 논문에서는 VTS 관제영역에 설치된 항로표지의 위치정보를 이용하여 2차원 해상감시레이더의 거리 및 방위각 오차를 보정하기 위한 방법을 제안한다. 2차원 레이더 측정값의 표준오차 모델과 항로표지 위치정보로부터 측정 잔차 모델을 유도하고, 레이더 시스템 오차를 추정하기 위한 선형 칼만필터를 설계한다. Monte-Carlo 모의실험을 통하여 제안한 방법을 검증하고, 항로표지 정보의 개수에 따른 레이더 시스템 오차 추정의 수렴 특성 및 정확도를 분석한다.

• 한국측량학회지
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• 제36권2호
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• pp.43-50
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• 2018

영상을 기반으로 하는 3차원 위치결정에서 카메라는 핵심적인 요소이며 이러한 카메라의 내부적인 특성을 제대로 결정하는 카메라 캘리브레이션 작업은 대상물의 3차원 좌표를 결정하기 위해서 필수적으로 선행되어야 할 과정이다. 본 연구에서는 캘리브레이션을 위한 체크보드의 크기와 형태에 영향을 받지 않고 반자동으로 카메라의 내부표정요소를 결정하는 방법론을 제안하였다. 제안한 방법론은 쿼터니언을 이용한 외부표정요소 추정, 캘리브레이션 타겟의 인식, 번들블록조정을 통한 내부표정요소 매개변수 결정으로 구성되어 있다. 체스보드 형태의 캘리브레이션 타겟을 이용하여 내부표정요소를 결정한 후 소규모 3차원 모형에 대한 3차원 위치를 결정하였으며 검사점을 이용한 정확도 평가를 통해서 수평위치와 수직위치 오차는 각각 약 ${\pm}0.006m$와 ${\pm}0.007m$를 얻을 수 있었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권9호
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• pp.4419-4441
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• 2016

A cause of vehicle accidents is the reduced visibility due to bad weather conditions such as fog. Therefore, an onboard vision system should take visibility detection into account. In this paper, we propose a simple and effective approach for measuring the visibility distance using a single camera placed onboard a moving vehicle. The proposed algorithm is controlled by a few parameters and mainly includes camera parameter estimation, region of interest (ROI) estimation and visibility computation. Thanks to the ROI extraction, the position of the inflection point may be measured in practice. Thus, combined with the estimated camera parameters, the visibility distance of the input foggy image can be computed with a single camera and just the presence of road and sky in the scene. To assess the accuracy of the proposed approach, a reference target based visibility detection method is also introduced. The comparative study and quantitative evaluation show that the proposed method can obtain good visibility detection results with relatively fast speed.

• 한국정밀공학회지
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• 제27권3호
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• pp.41-49
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• 2010

Two promising control candidates have been selected to test the sinusoidal reference tracking performance for a brush-type polishing machine having strong nonlinearities and disturbances. The controlled target system is an oscillating mechanism consisting of a common positioning stage of one degree-of-freedom with a screw and a ball nut driven by a servo motor those can be obtained commercially. Beside the strong nonlinearity such as stick-slip friction, the periodic contact of the polishing brush and the work piece adds an external disturbance. Selected control candidates are a Sliding Mode Control (SMC) and a variant of a feedback linearization control called Smooth Robust Nonlinear Control (SRNC). A SMC and SRNC are selected since they have good theoretical backgrounds, are suitable to be implemented in a digital environment and show good disturbance and modeling uncertainty rejection performance. It should be also noted that SRNC has a nobel approach in that it uses the position information to compensate the stickslip friction. For both controllers analytical and experimental studies have been conducted to show control design approaches and to compare the performance against the strong nonlinearity and the disturbances.

• International Journal of Control, Automation, and Systems
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• 제6권2호
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• pp.204-213
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• 2008

In this paper, we present the practical application of an Unscented Kalman Filter (UKF) for an Indoor Mobile Localization System using ultrasonic sensors. It is true that many kinds of localization techniques have been researched for several years in order to contribute to the realization of a ubiquitous system; particularly, such a ubiquitous system needs a high degree of accuracy to be practical and efficient. Unfortunately, a number of localization systems for indoor space do not have sufficient accuracy to establish any special task such as precise position control of a moving target even though they require comparatively high developmental cost. Therefore, we developed an Indoor Mobile Localization System having high localization performance; specifically, the Unscented Kalman Filter is applied for improving the localization accuracy. In addition, we also present the additive filter named 'Pre-filtering' to compensate the performance of the estimation algorithm. Pre-filtering has been developed to overcome negative effects from unexpected external noise so that localization through the Unscented Kalman Filter has come to be stable. Moreover, we tried to demonstrate the performance comparison of the Unscented Kalman Filter and another estimation algorithm, such as the Unscented Particle Filter (UPF), through simulation for our system.