• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2002년도 Proceedings of International Symposium on Remote Sensing
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• pp.292-296
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• 2002

This study has been accomplished as a experimental study for field application of 3D Perspective Image Map creation using Digital Topographical Map and based on the Ortho-Projection Image which is generated from Satellite Overlay Images and the precise Relative Coordinates of longitude, latitude and altitude which is corrected by GCP(Ground Control Point). AS to Contour Lines Map which is created by Coordinate conversion of 1:5,000 Topographical Map, we firstly made Satellite Image Map to substitute for Digital Topographical Map through overlapping the original images on top of each Ortho-Projection Image created and checking the accuracy. In addition to 3D Image Map creation for 3D Terrain analysis of a target district, Slope Gradient Analysis, Aspect Analysis and Terrain Elevation Model generation, multidirectional 3D Image generation by DEM can be carried out through this study. This study is to develop a mapping technology with which we can generate 3D Satellite Images of a target district through the composition of Digital Maps and Facility Blueprint and arbitrarily create 3D Perspective Images of the target district from any view point.

• 제어로봇시스템학회논문지
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• 제20권11호
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• pp.1170-1174
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• 2014

This paper suggests using the ICP (Iterative Closet Point) algorithm to compensate a two-dimensional map. ICP algorithm is a typical algorithm method using matching distance data. When building a two-dimensional map, using data through the value of a laser scanner, it occurred warping and distortion of a two-dimensional map because of the difference of distance from the value of the sensor. It uses the ICP algorithm in order to reduce any error of line. It validated the proposed method through experiment involving matching a two-dimensional map based reference data and measured the two-dimensional map.

• 한국멀티미디어학회논문지
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• 제22권4호
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• pp.417-423
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• 2019

This paper presents an evaluation system to determine the completeness of a space map obtained by a visual SLAM(Simultaneous Localization And Mapping) algorithm. The proposed system consists of three parts. First, the proposed system detects the occurrence of loop closing to confirm that users acquired the information from all directions. Thereafter, the acquired map is divided with regular intervals and is verified whether each area has enough map points to successfully estimate users' position. Finally, to check the effectiveness of each map point, the system checks whether the map points are identifiable even at the location where there is a large distance difference from the acquisition position. Experimental results show that space maps whose completeness is proven by the proposed system has higher stability and accuracy in terms of position estimation than other maps that are not proven.

• Journal of Computational Design and Engineering
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• 제1권1호
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• pp.48-54
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• 2014

In this paper, the authors propose a system for assisting mold designers of plastic parts. With a CAD model of a part, the system automatically determines the optimal ejecting direction of the part with minimum undercuts. Since plastic parts are generally very thin, many rib features are placed on the inner side of the part to give sufficient structural strength. Our system extracts the rib features from the CAD model of the part, and determines the possible ejecting directions based on the geometric properties of the features. The system then selects the optimal direction with minimum undercuts. Possible ejecting directions are represented as discrete points on a Gauss map. Our new point distribution method for the Gauss map is based on the concept of the architectural geodesic dome. A hierarchical structure is also introduced in the point distribution, with a higher level "rough" Gauss map with rather sparse point distribution and another lower level "fine" Gauss map with much denser point distribution. A system is implemented and computational experiments are performed. Our system requires less than 10 seconds to determine the optimal ejecting direction of a CAD model with more than 1 million polygons.

• 한국측량학회지
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• 제37권2호
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• pp.45-53
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• 2019

RGB-D SLAM (Simultaneous Localization and Mapping) refers to the technology of using deep camera as a visual sensor for SLAM. In view of the disadvantages of high cost and indefinite scale in the construction of maps for laser sensors and traditional single and binocular cameras, a method for creating three-dimensional map of indoor environment with deep environment data combined with RGB-D SLAM scheme is studied. The method uses a mobile robot system equipped with a consumer-grade RGB-D sensor (Kinect) to acquire depth data, and then creates indoor three-dimensional point cloud maps in real time through key technologies such as positioning point generation, closed-loop detection, and map construction. The actual field experiment results show that the average error of the point cloud map created by the algorithm is 0.0045m, which ensures the stability of the construction using deep data and can accurately create real-time three-dimensional maps of indoor unknown environment.

• 자동차안전학회지
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• 제14권2호
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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.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2010년도 추계학술대회
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• pp.343-344
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• 2010

마커리스 시스템의 경우 2차원 영상에서 깊이 값을 추정하기 위해서는 스테레오 비젼과 같이 고가의 장비를 통해 깊이 값을 추정하였다. 이에 단안 영상에서 깊이 값을 추정하여 객체를 증강하기 위해 소실점을 추출하고 상대적 깊이 값을 추정한다. 객체 증강에 있어 향상된 몰입감을 얻기 위해서는 가상의 객체들이 거리에 따라 서로 다른 크기로 그려져야 한다. 본 논문에서는 획득한 영상에서 소실점을 생성하고 깊이정보를 이용하여 증강된 객체를 서로 다른 크기로 증강하여 객체간 상호 몰입감을 향상시켰다.

• 자동차안전학회지
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• 제11권2호
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• pp.29-34
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• 2019

This paper presents a new method for construction of a static obstacle map. A static obstacle is important since it is utilized to path planning and decision. Several established approaches generate static obstacle map by grid method and counting algorithm. However, these approaches are occasionally ineffective since the density of LiDAR layer is low. Our approach solved this problem by applying probability theory. First, we converted all LiDAR point to Gaussian distribution to considers an uncertainty of LiDAR point. This Gaussian distribution represents likelihood of obstacle. Second, we modeled dynamic transition of a static obstacle map by adopting the Hidden Markov Model. Due to the dynamic characteristics of the vehicle in relation to the conditions of the next stage only, a more accurate map of the obstacles can be obtained using the Hidden Markov Model. Experimental data obtained from test driving demonstrates that our approach is suitable for mapping static obstacles. In addition, this result shows that our algorithm has an advantage in estimating not only static obstacles but also dynamic characteristics of moving target such as driving vehicles.

• 로봇학회논문지
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• 제18권4호
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• pp.487-495
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• 2023

This paper presents a method for autonomous exploration of indoor environments using a 2-dimensional Light Detection And Ranging (LiDAR) scanner. The proposed frontier-based exploration method considers navigability from the current robot position to extracted frontier targets. An approach to constructing the point cloud grid map that accurately reflects the occupancy probability of glass obstacles is proposed, enabling identification of safe frontier grids on the safety grid map calculated from the point cloud grid map. Navigability, indicating whether the robot can successfully navigate to each frontier target, is calculated by applying the skeletonization-informed rapidly exploring random tree algorithm to the safety grid map. While conventional exploration approaches have focused on frontier detection and target position/direction decision, the proposed method discusses a safe navigation approach for the overall exploration process until the completion of mapping. Real-world experiments have been conducted to verify that the proposed method leads the robot to avoid glass obstacles and safely navigate the entire environment, constructing the point cloud map and calculating the navigability with low computing time deviation.

• 대한수학회지
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• 제34권3호
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• pp.719-730
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• 1997

From a minimax inequality related to admissible multimaps, we deduce generalized versions of lopsided saddle point theorems, fixed point theorems, existence of maximizable linear functionals, the Walras excess demand theorem, and the Gale-Nikaido-Debreu theorem.