• Proceedings of the KSME Conference
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• 2003.04a
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• pp.822-827
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• 2003

Topological maps have drawn more attention recently because they are compact, provide natural interfaces, and are applicable to path planning easily. To build a topological map incrementally, Voronoi diagram was used by many researchers. The Voronoi diagram, however, has difficulty in applying to arbitrarily shaped objects and needs long computation time. In this paper, we present a new method for global topological map from the local topological maps incrementally. The local topological maps are created through a thinning algorithm from a local grid map, which is built based on the sensor information at the current robot position. A thinning method requires simpler computation than the Voronoi diagram. Localization based on the topological map is usually difficult, but additional nodes created by the thinning method can improve localization performance. A series of experiments have been conducted using a two-wheeled mobile robot equipped with a laser scanner. It is shown that the proposed scheme can create satisfactory topological maps.

• Korean Journal of Remote Sensing
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• v.24 no.2
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• pp.179-188
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• 2008

This study aims for generating a city spatial model required for the creation of a 3D noise map. In this study, we propose an efficient method to generate 3D models of the terrain and buildings using only a digital map and draft maps previously established without using any sensory data. The terrain model is generated by interpolating into a grid the elevation values derived from both the contour lines and the elevation point of the digital map. Building model is generated by combining the 2D building boundaries and the building elevations extracted from the digital map and the draft map, respectively. This method has been then applied to a digital map and three sets of draft maps created in the different times. covering the entire area of Yeongdeungpo-gu. The generated city spatial model has been successfully utilized for the noise analysis and the 3D visualization of the analysis results.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1931-1935
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• 2004

A location estimate problem is critical issues for mobile robot. Because it is basic problem in practical use of the mobile robot which do what, or move where, or reach an aim. Already there are many technologies of robot localization (like GPS, vision, sonar sensor, etc) used on development. But the elevation of accurateness was brought the problem that must consider an increase of a hardware cost and addition electric power in each ways. There is the core in question to develop available and accurate sensing algorithm though it is economical. We used a ultrasonic sensor and was going to implement comparatively accurate localization though economical. Using a sensing data, we could make a grid map and estimate a position of a mobile robot. In this paper, to get a satisfactory answer about this problem using a ultrasonic sensor.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.10
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• pp.965-971
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• 2015

This paper suggests a feature point-based Iterative Closest Point (ICP) algorithm to compensate for the disparity error in building a two-dimensional map. The ICP algorithm is a typical algorithm for matching a common object in two different images. In the process of building a two-dimensional map using the laser scanner data, warping and distortions exist in the map because of the disparity between the two sensor values. The ICP algorithm has been utilized to reduce the disparity error in matching the scanned line data. For this matching process in the conventional ICP algorithm, pre-known reference data are required. Since the proposed algorithm extracts characteristic points from laser-scanned data, reference data are not required for the matching. The laser scanner starts from the right side of the mobile robot and ends at the left side, which causes disparity in the scanned line data. By finding the matching points between two consecutive frame images, the motion vector of the mobile robot can be obtained. Therefore, the disparity error can be minimized by compensating for the motion vector caused by the mobile robot motion. The validity of the proposed algorithm has been verified by comparing the proposed algorithm in terms of map-building accuracy to conventional ICP algorithm real experiments.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.6
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• pp.14-22
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• 2014

An autonomous vehicle requires a technology of generating maps by recognizing surrounding environment. The recognition of the vehicle's environment can be achieved by using distance information from a 2D laser scanner and color information from a camera. Such sensor information is used to generate 2D or 3D maps. A 2D map is used mostly for generating routs, because it contains information only about a section. In contrast, a 3D map involves height values also, and therefore can be used not only for generating routs but also for finding out vehicle accessible space. Nevertheless, an autonomous vehicle using 3D maps has difficulty in recognizing environment in real time. Accordingly, this paper proposes the technology for generating 2D maps that guarantee real-time recognition. The proposed technology uses only the color information obtained by removing height values from 3D maps generated based on the fusion of 2D laser scanner and camera data.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.136-139
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• 2001

This paper develops a sensor based navigation method that utilizes fuzzy logic and the Dempster-Shafer evidence theory for mobile robot in uncertain environment. The proposed navigator consists of two behaviors: obstacle avoidance and goal seeking. To navigate reliably in the environment, we make a map building process before the robot finds a goal position and create a robust fuzzy controller. In this paper, the map is constructed on a two-dimensional occupancy grid. The sensor readings are fused into the map using D-S inference rule. Whenever the robot moves, it catches new information about the environment and replaces the old map with new one. With that process the robot can go wandering and finding the goal position. The usefulness of the proposed method is verified by a series of simulations. This paper deals with the fuzzy modeling for the complex and uncertain nonlinear systems, in which conventional and mathematical models may fail to give satisfactory results. Finally, we provide numerical examples to evaluate the feasibility and generality of the proposed method in this paper.

• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.179-182
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• 2008

This study simulated the flood inundations of the Nakdong River catchment running through Yangsan, a small city located in the south eastern area of Korea by using the depth averaged two-dimensional hydrodynamic numerical model. The numerical model employs the staggered grid system including moving boundary and a finite different method to solve the Saint-Venant equations. A second order upwind scheme is used to discretize the nonlinear convection terms of the momentum equations, whereas linear terms are discretized by a first order leap-frog scheme(Cho and Yoon, 1998). The numerical model was applied to a real topography to simulate the flood inundation of the Yangsan basin. The numerical results for urban district are visualized in three dimension. These results can be essentially utilized to construct the three dimensional inundation map after building the GIS-based database in local public organizations in order to protect the life and property safely.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2021.06a
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• pp.283-285
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• 2021

본 논문에서는 수중에서 선박 하부에 붙은 오손생물(fouling organism)을 탐지하고 격자지도(grid map)로 나타내는 시스템을 제안한다. 제안하는 시스템은 소나(sound navigation and ranging: SONAR) 센서와 오손생물사이의 시간 데이터를 수집한 후, 라즈베리 파이 4(raspberry pi 4)에서 수집된 데이터를 이용해 격자지도에 맵핑(mapping)함으로써, 선저하부의 상태를 파악하는데 도움을 줄 수 있다. 본 논문에서는 제안된 지도 시스템을 이용하여 선박 하부에 붙은 오손생물의 분포를 확인할 수 있다.

• Journal of KIBIM
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• v.10 no.4
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• pp.32-39
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• 2020

In recent years, the construction industry is getting bigger and more complex, so it is becoming difficult to acquire point cloud data for construction equipments and workers. Point cloud data is measured using a drone and MMS(Mobile Mapping System), and the collected point cloud data is used to create a 3D digital map. In particular, the construction site is located at outdoors and there are many irregular terrains, making it difficult to collect point cloud data. For these reasons, adopting a noise reduction algorithm suitable for the characteristics of the construction industry can affect the improvement of the analysis accuracy of digital maps. This is related to various environments and variables of the construction site. Therefore, this study reviewed and analyzed the existing research and techniques on the noise reduction algorithm. And based on the results of literature review, performance evaluation of major noise reduction algorithms was conducted for digital maps of construction sites. As a result of the performance evaluation in this study, the voxel grid algorithm showed relatively less execution time than the statistical outlier removal algorithm. In addition, analysis results in slope, space, and earth walls of the construction site digital map showed that the voxel grid algorithm was relatively superior to the statistical outlier removal algorithm and that the noise removal performance of voxel grid algorithm was superior and the object preservation ability was also superior. In the future, based on the results reviewed through the performance evaluation of the noise reduction algorithm of this study, we will develop a noise reduction algorithm for 3D point cloud data that reflects the characteristics of the construction site.

• The Journal of Korea Robotics Society
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• v.13 no.2
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• pp.79-85
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• 2018

This study focuses on autonomous exploration based on map expansion for an underwater robot equipped with acoustic sonars. Map expansion is applicable to large-area mapping, but it may affect localization accuracy. Thus, as the key contribution of this paper, we propose a method for underwater autonomous exploration wherein the robot determines the trade-off between map expansion ratio and position accuracy, selects which of the two has higher priority, and then moves to a mission step. An occupancy grid map is synthesized by utilizing the measurements of an acoustic range sonar that determines the probability of occupancy. This information is then used to determine a path to the frontier, which becomes the new search point. During area searching and map building, the robot revisits artificial landmarks to improve its position accuracy as based on imaging sonar-based recognition and EKF-SLAM if the position accuracy is above the predetermined threshold. Additionally, real-time experiments were conducted by using an underwater robot, yShark, to validate the proposed method, and the analysis of the results is discussed herein.