• Journal of Mechanical Science and Technology
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• v.17 no.10
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• pp.1411-1422
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• 2003

The detection of free spaces between obstacles in a scene is a prerequisite for navigation of a mobile robot. Especially for stereo vision-based navigation, the problem of correspondence between two images is well known to be of crucial importance. This paper describes multi-range approach of area-based stereo matching for grid mapping and visual navigation in uncertain environment. Camera calibration parameters are optimized by evolutionary algorithm for successful stereo matching. To obtain reliable disparity information from both images, stereo images are to be decomposed into three pairs of images with different resolution based on measurement of disparities. The advantage of multi-range approach is that we can get more reliable disparity in each defined range because disparities from high resolution image are used for farther object a while disparities from low resolution images are used for close objects. The reliable disparity map is combined through post-processing for rejecting incorrect disparity information from each disparity map. The real distance from a disparity image is converted into an occupancy grid representation of a mobile robot. We have investigated the possibility of multi-range approach for the detection of obstacles and visual mapping through various experiments.

• Journal of the Korean Society for Precision Engineering
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• v.32 no.6
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• pp.543-551
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• 2015

In this study, a novel method for dimension measurement of large-scale moving objects using stereo camera with 2-degree of freedom (2-DOF) mechanism is presented. The proposed method utilizes both the advantages of stereo vision technique and the enlarged visibility range of camera due to 2-DOF rotary mechanism in measuring large-scale moving objects. The measurement system employs a stereo camera combined with a 2-DOF rotary mechanism that allows capturing separate corners of the measured object. The measuring algorithm consists of two main stages. First, three-dimensional (3-D) positions of the corners of the measured object are determined based on stereo vision algorithms. Then, using the rotary angles of the 2-DOF mechanism the dimensions of the measured object are calculated via coordinate transformation. The proposed system can measure the dimensions of moving objects with relatively slow and steady speed. We showed that the proposed system guarantees high measuring accuracy with some experiments.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.1907-1910
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• 2003

This paper describes a method of stereo image composition for mixed reality without camera calibration or complicate tracking algorithm. The proposed system tracks the panel which has blob makers, and composes virtual objects naturally using the method of texture mapping which is often used in geological computer graphics mapping when we do mapping 2D computer graphic data or man-made 2D images. The proposed algorithm makes it possible for us to compose virtual data even in the case that the panel is bent. For composing 3D object, the system uses depth information obtained from stereo image so that we do not need cumbersome procedure of camera calibration.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.3
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• pp.27-33
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• 2003

The image-based 3D modeling is the technique of generating a 3D graphic model from images acquired using cameras. It is being researched as an alternative technique for the expensive 3D scanner. In this paper, we propose the image-based, 3D modeling system using calibrated stereo cameras. The proposed algorithm for rendering, 3D model consists of three steps, camera calibration, 3D reconstruction, and 3D registration step. In the camera calibration step, we estimate the camera matrix for the image aquisition camera. In the 3D reconstruction step, we calculate 3D coordinates using triangulation from corresponding points of the stereo image. In the 3D registration step, we estimate the transformation matrix that transforms individually reconstructed 3D coordinates to the reference coordinate to render the single 3D model. As shown the result, we generated relatively accurate 3D model.

• The Journal of Korea Robotics Society
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• v.4 no.4
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• pp.298-304
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• 2009

This paper describes an algorithm that improves 3D reconstruction result using a multi-sensor fusion disparity map. We can project LRF (Laser Range Finder) 3D points onto image pixel coordinatesusing extrinsic calibration matrixes of a camera-LRF (${\Phi}$, ${\Delta}$) and a camera calibration matrix (K). The LRF disparity map can be generated by interpolating projected LRF points. In the stereo reconstruction, we can compensate invalid points caused by repeated pattern and textureless region using the LRF disparity map. The result disparity map of compensation process is the multi-sensor fusion disparity map. We can refine the multi-sensor 3D reconstruction based on stereo vision and LRF using the multi-sensor fusion disparity map. The refinement algorithm of multi-sensor based 3D reconstruction is specified in four subsections dealing with virtual LRF stereo image generation, LRF disparity map generation, multi-sensor fusion disparity map generation, and 3D reconstruction process. It has been tested by synchronized stereo image pair and LRF 3D scan data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.5
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• pp.1157-1163
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• 2000

In this paper, we present a novel and practical stereo vision system that uses only one camera and four mirrors placed in front of the camera. The equivalent of a stereo pair of images are formed as left and right halves of a single CCD image by using four mirrors placed in front of the ten of a CCD camera. An object arbitrary point in 3D space is transformed into two virtual points by the four mirrors. As in the conventional stereo system, the displacement between the two conjugate image points of the two virtual points is directly related to the depth of the object point. This system has the following advantages over traditional two camera stereo that identical system parameters, easy calibration and easy acquisition of stereo data.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.590-593
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• 2001

In this paper we describe the 6-axes robot's position determination using a stereo vision and an image based control method. When use a stereo vision, it need a additional time to compare with mono vision system. So to reduce the time required, we use the stereo vision not image Jacobian matrix estimation but depth estimation. Image based control is not needed the high-precision of camera calibration by using a image Jacobian. The experiment is executed as devide by two part. The first is depth estimation by stereo vision and the second is robot manipulator's positioning.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.30-32
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• 2004

With 3-D vision measuring, camera calibration is necessary to calculate parameters accurately. Camera calibration was developed widely in two categories. The first establishes reference points in space, and the second uses a grid type frame and statistical method. But, the former has difficulty to setup reference points and the latter has low accuracy. In this paper we present an algorithm for camera calibration using perspective ratio of the grid type frame with different line widths. It can easily estimate camera calibration parameters such as lens distortion, focal length, scale factor, pose, orientations, and distance. The advantage of this algorithm is that it can estimate the distance of the object. Also, the proposed camera calibration method is possible estimate distance in dynamic environment such as autonomous navigation. To validate proposed method, we set up the experiments with a frame on rotator at a distance of 1, 2, 3, 4[m] from camera and rotate the frame from -60 to 60 degrees. Both computer simulation and real data have been used to test the proposed method and very good results have been obtained. We have investigated the distance error affected by scale factor or different line widths and experimentally found an average scale factor that includes the least distance error with each image. The average scale factor tends to fluctuate with small variation and makes distance error decrease. Compared with classical methods that use stereo camera or two or three orthogonal planes, the proposed method is easy to use and flexible. It advances camera calibration one more step from static environments to real world such as autonomous land vehicle use.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.268-272
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• 1996

Error correction effect for maladjusted stereo cameras with calibrated pixel distance parameter is presented. The camera calibration is a necessary procedure for stereo vision-based depth computation. Intra and extra parameters should be obtain to determine the relation between image and world coordination through experiment. One difficulty is in camera alignment for parallel installation: placing two CCD arrays in a plane. No effective methods for such alignment have been presented before. Some amount of depth error caused from such non-parallel installation of cameras is inevitable. If the pixel distance parameter which is one of intra parameter is calibrated with known points, such error can be compensated in some amount. Such error compensation effect with the calibrated pixel distance parameter is demonstrated with some experimental results.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.507-510
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• 2003

In this paper, optical sensor system using PSD(Position Sensitive Detection) is proposed to obtain the three dimensional position of moving markers attached to human body. To find the coordinates of an moving marrer with stereo vision system, two different sight rays of an moving marker are required. Usually, those are acquired with two optical sensors synchronized at the same time. PSD sensor is used to measure the position of an incidence light in real-time. To get the three-dimension position of light source on moving markers, a conventional camera calibration method are used. In this research, we realized a low cost motion capture system. The proposed system shows high three-dimension measurement accuracy and fast sampling frequency.