• Journal of Welding and Joining
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• v.19 no.2
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• pp.200-207
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• 2001

Three-diemnsional bead profile was measured using the biprism stereo vision sensor in GMAW, which consists of an optical filter, biprism and CCD camera. Since single CCD camera is used, this system has various advantages over the conventional stereo vision system using two cameras such as finding the corresponding points along the horizontal scanline. In this wort, the biprism stereo vision sensor was designed for the GMAW, and the linear calibration method was proposed to determine the prism and camera parameters. Image processing techniques were employed to find the corresponding point along the pool boundary. The ism-intensity contour corresponding to the pool boundary was found in the pixel order and the filter-based matching algorithm was used to refine the corresponding points in the subpixel order. Predicted bead dimensions were in broad agreements with the measured results under the conditions of spray mode and humping bead.

• Journal of Advanced Marine Engineering and Technology
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• v.36 no.5
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• pp.674-682
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• 2012

A new intelligent stereo vision sensor system was studied for the motion and depth control of unmanned vehicles. A new bottom-up saliency map model for the human-like active stereo vision system based on biological visual process was developed to select a target object. If the left and right cameras successfully find the same target object, the implemented active vision system with two cameras focuses on a landmark and can detect the depth and the direction information. By using this information, the unmanned vehicle can approach to the target autonomously. A number of tests for the proposed bottom-up saliency map were performed, and their results were presented.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2200-2202
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• 1998

Autonomous mobile robot provide many functions such as sensing, processing, and driving. For more intelligent jobs, more intelligent functions are to be added and the existing functions may be updated. To execute a job autonomous mobile robot has a information of surrounding environment. So, robot uses sonar sensor, vision sensor and so on. Obtained sensor information is used map generation. This paper is focused on map generation using stereo vision system.

• Journal of Sensor Science and Technology
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• v.6 no.2
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• pp.123-130
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• 1997

In this paper, we present an original stereo vision system which comprises two process: 1. An image segmentation algorithm based on new concept called declivity and using automatic thresholds. 2. A new stereo matching algorithm based on an optimal path search. This path is obtained by dynamic programming method which uses the threshold values calculated during the segmentation process. At present, a complete depth map of indoor scene only needs about 3 s on a Sun workstation IPX, and this time will be reduced to a few tenth of second on a specialised architecture based on several DSPs which is currently under consideration.

• 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.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.454-457
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• 2004

AGV for FMS must be detected an obstacle. Therefore, many studies have been advanced, and recently, the ultra sonic sensor is used for this. However, the new method has to be developed because the ultra-sonic-sensor has many problems as a noise in factory, an directional error and detection of the obstacle size. So, we study the fast stereo vision system that can give more information to obstacles for intelligent AGV system. For this, the simulated AGV system was made with two CCD cameras in front to get the stereo images, and the threshold process by color information (intensity and chromaticity) and structure stereo matching method were constructed.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.4
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• pp.679-684
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• 2019

We propose a method to measure the number of specific species of class using CAFFE model and a method to measure length and width of object using stereo vision. To obtain the width of an object, the location coordinates of objects appearing on the left and right sensor is compared and the distance from the sensor to the object is obtained. Then the length of the object in the image by using the distance and the approximate value of the actual length of the object is calculated.

• Journal of Advanced Navigation Technology
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• v.17 no.3
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• pp.354-360
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• 2013

A stereo vision analysis was performed for motion and depth control of unmanned vehicles. In stereo vision, the depth information in three-dimensional coordinates can be obtained by triangulation after identifying points between the stereo image. However, there are always triangulation errors due to several reasons. Such errors in the vision triangulation can be alleviated by careful arrangement of the camera position and orientation. In this paper, an approach to the determination of the optimal position and orientation of camera is presented for unmanned vehicles.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.5
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• pp.2400-2405
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• 2013

We detect the obstacle for the UGV(unmanned ground vehicle) from the compound image which is generated by stereo vision sensor masking the depth image and color image. Stereo vision sensor can gathers the distance information by stereo camera. The obstacle information from the depth compound image can be send to mobile robot and the robot can localize the indoor area. And, we test the performance of the mobile robot in terms of distance between the obstacle and the robot's position and also test the color, depth and compound image respectively. Moreover, we test the performance in terms of number of frame per second which is processed by operating machine. From the result, compound image shows the improved performance in distance and number of frames.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.3
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• pp.240-247
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• 2013

This paper proposes an optimal three-dimensional coordinate implementation of the vision sensor using two CCD cameras. The PBVS (Position based visual servoing) is implemented using the positional information obtained from images. Stereo vision by PBVS method that has enhanced every frame using calibration parameters is effective in the distance calculation. The IBVS (Image based visual servoing) is also implemented using the difference between reference and obtained images. Stereo vision by IBVS method calculates the distance using rotation angle of motors that correspond eyes and neck without enhanced images. The PBVS method is compared with the IBVS method in terms of advantages, disadvantages, computing time, and performances. Finally, the IBVS method is applied for the dual arm manipulator on the mobile inverted pendulum. The autonomous mobile inverted pendulum is successfully demonstrated using the center of the manipulator's mass.