• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.1
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• pp.123-128
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• 2018

In this paper, we propose a multi-vehicle object detection algorithm using feature point matching that tracks efficient vehicle objects. The proposed algorithm extracts the feature points of the vehicle using the FAST algorithm for efficient vehicle object tracking. And True if the feature points are included in the image segmented into the 5X5 region. If the feature point is not included, it is processed as False and the corresponding area is blacked to remove unnecessary object information excluding the vehicle object. Then, the post processed area is set as the maximum search window size of the vehicle. And A minimum search window using the outermost feature points of the vehicle is set. By using the set search window, we compensate the disadvantages of the search window size of mean-shift algorithm and track vehicle object. In order to evaluate the performance of the proposed method, SIFT and SURF algorithms are compared and tested. The result is about four times faster than the SIFT algorithm. And it has the advantage of detecting more efficiently than the process of SUFR algorithm.

• Journal of the Korea Computer Industry Society
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• v.10 no.5
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• pp.227-234
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• 2009

The Paper introduces a new technique for block matching motion estimation. since the temporal correlation of a animation sequence between the motion vector of current block and the motion vector of previous block. In this paper, we propose the scene change detection algorithm for block matching using the temporal correlation of the animation sequence and the center-biased property of motion vectors. The proposed algorithm determines the location of a better starting point for the search of an exact motion vector using the point of the smallest SAD(sum of absolute difference) value by the predicted motion vector from the same block of the previous frame and the predictor candidate point on each search region. Simulation results show that the PSNR values are improved as high as 9~32% in terms of average number of search point per motion vector estimation and improved about 0.06~0.21dB on an average except the FS(full search) algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.4C
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• pp.407-416
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• 2006

It is difficult for the movement detection of an object through a camera to detect exact movement because of unnecessary noises and changes of the light. It can be recognized as a background, when there is no movement after the inflow of an object. Therefore, It is necessary to fast search algorithm for tracking and extract of object that is realtime image. In this thesis, we evaluate the difference of the input vision based on initial image and replace some pixels in process of time. When there is a big difference between background image and input image, we decide it is the point of the time of the object input and then extract boundary point of it. The extracted boundary point detects precise movement of the object by creating minimum block of it and searching block that maintaining distance. The designed and embodied system shows more than 95% accuracy in the performance test.

• Journal of Auto-vehicle Safety Association
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• v.14 no.2
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• pp.39-44
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• 2022

This paper presents LiDAR static obstacle map based vehicle position correction algorithm for urban autonomous driving. Real Time Kinematic (RTK) GPS is commonly used in highway automated vehicle systems. For urban automated vehicle systems, RTK GPS have some trouble in shaded area. Therefore, this paper represents a method to estimate the position of the host vehicle using AVM camera, front camera, LiDAR and low-cost GPS based on Extended Kalman Filter (EKF). Static obstacle map (STOM) is constructed only with static object based on Bayesian rule. To run the algorithm, HD map and Static obstacle reference map (STORM) must be prepared in advance. STORM is constructed by accumulating and voxelizing the static obstacle map (STOM). The algorithm consists of three main process. The first process is to acquire sensor data from low-cost GPS, AVM camera, front camera, and LiDAR. Second, low-cost GPS data is used to define initial point. Third, AVM camera, front camera, LiDAR point cloud matching to HD map and STORM is conducted using Normal Distribution Transformation (NDT) method. Third, position of the host vehicle position is corrected based on the Extended Kalman Filter (EKF).The proposed algorithm is implemented in the Linux Robot Operating System (ROS) environment and showed better performance than only lane-detection algorithm. It is expected to be more robust and accurate than raw lidar point cloud matching algorithm in autonomous driving.

• Journal of the Society of Naval Architects of Korea
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• v.55 no.1
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• pp.66-74
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• 2018

In order to evaluate the precision degree of the blocks on the dock, the shipyards recently started to use the point cloud approaches using the 3D scanners. However, they hesitate to use it due to the limited time, cost, and elaborative effects for the post-works. Although it is somewhat traditional instead, they have still used the electro-optical wave devices which have a characteristic of having less dense point set (usually 1 point per meter) around the contact section of two blocks. This paper tried to expand the usage of point sets. Our approach can estimate the rework time to weld between the Pre-Erected(PE) Block and Erected(ER) block as well as the precision of block construction. In detail, two algorithms were applied to increase the efficiency of estimation process. The first one is K-mean clustering algorithm which is used to separate only the related contact point set from others not related with welding sections. The second one is the Concave hull algorithm which also separates the inner point of the contact section used for the delayed outfitting and stiffeners section, and constructs the concave outline of contact section as the primary objects to estimate the rework time of welding. The main purpose of this paper is that the rework cost for welding is able to be obtained easily and precisely with the defective point set. The point set on the blocks' outline are challenging to get the approximated mathematical curves, owing to the lots of orthogonal parts and lack of number of point. To solve this problems we compared the Radial based function-Multi-Layer(RBF-ML) and Akima interpolation method. Collecting the proposed methods, the paper suggested the noble point matching method for minimizing the rework time of block-welding on the dock, differently the previous approach which had paid the attention of only the degree of accuracy.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.1
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• pp.121-127
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• 2008

In this paper, we propose a new 3-D working point determination method for industrial robot using vision camera system and block interpolation technique with feature points in a vehicle body. To detect the feature points in a vehicle body, we applied the pattern matching method. For determination of working point, we applied block interpolation method. The block consists of 3-D type blocks with detected feature points per section. 3-D position is selected by Euclidean distance between 245 feature values and an acquired feature point. In order to evaluate the proposed algorithm, experiments are performed in glass equipment process in real industrial vehicle assembly line.

• Journal of Electrical Engineering and Technology
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• v.10 no.2
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• pp.676-687
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• 2015

Path planning algorithms are used to allow mobile robots to avoid obstacles and find ways from a start point to a target point. The general path planning algorithm focused on constructing of collision free path. However, a high continuous path can make smooth and efficiently movements. To improve the continuity of the path, the searched waypoints are connected by the proposed polynomial interpolation. The existing polynomial interpolation methods connect two points. In this paper, point groups are created with three points. The point groups have each polynomial. Polynomials are made by matching the differential values and simple matrix calculation. Membership functions are used to distribute the weight of each polynomial at overlapped sections. As a result, the path has $G^2$ continuity. In addition, the proposed method can analyze path numerically to obtain curvature and heading angle. Moreover, it does not require complex calculation and databases to save the created path.

• Proceedings of the KOSOMBE Conference
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• v.1990 no.05
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• pp.21-24
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• 1990

This paper presents a new algorithm for P-wave detection in the ECG signal, we get the peak, onset and offset point by using significant point extraction algorithm with 5-point derivative. To these set of extracted significant points, we apply amplitude and duration threshold criterion. we define the set of significant point meeting the criterion as P-wave candidate. Then P-wave candidate is classified through match-process with template. The template with maximum number or P-wave candidate is selected to be the P-wave.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.8 s.251
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• pp.906-915
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• 2006

This study concerns an advanced 3D surface reconstruction method that the vertices of surface model can be completely matched to the unstructured point cloud measured from arbitrary complex shapes. The concept of bounding voxel model is introduced to generate the mesh model well-representing the geometrical and topological characteristics of point cloud. In the reconstruction processes, the application of various methodologies such as shrink-wrapping, mesh simplification, local subdivision surface fitting, insertion of is isolated points, mesh optimization and so on, are required. Especially, the effectiveness, rapidity and reliability of the proposed surface reconstruction method are demonstrated by the simulation results for the geometrically and topologically complex shapes like dragon and human mouth.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.3
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• pp.235-241
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• 2007

This paper proposes a self-calibration method of robots those are used in industrial assembly lines. The proposed method is a position compensation using laser sensor and vision camera. Because the laser sensor is cross type laser sensor which can scan a horizontal and vertical line, it is efficient way to detect a feature of vehicle and winding shape of vehicle's body. For position compensation of 3-Dimensional axis, we applied block interpolation method. For selecting feature point, pattern matching method is used and 3-D position is selected by Euclidean distance mapping between 462 feature values and evaluated feature point. In order to evaluate the proposed algorithm, experiments are performed in real industrial vehicle assembly line. In results, robot's working point can be displayed 3-D points. These points are used to diagnosis error of position and reselecting working point.