• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.174-179
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• 2003

Through the simultaneous localization and map building (SLAM) technique, a robot can create maps about its unknown environment while it continuously localizes its position. Grid maps and feature maps have been widely used for SLAM together with application of probability methods and POMDP (partially observed Markov decision process). But this approach based on grid maps suffers from enormous computational burden. Topological maps, however, have drawn more attention these days because they are compact, provide natural interfaces, and are easily applicable to path planning in comparison with grid maps. Some topological SLAM techniques like GVG (generalized Voronoi diagram) were introduced, but it enables the robot to decide only whether the current position is part of GVG branch or not in the GVG algorithm. In this paper, therefore, to overcome these problems, we present a method for updating a global topological map from the local topological maps. These local topological maps are created through a labeled Voronoi diagram algorithm from the local grid map built based on the sensor information at the current robot position. And the nodes of a local topological map can be utilized as the features of the environment because it is robust in light of visibility problem. The geometric information of the feature is applied to the extended Kalman filter and the SLAM in the indoor environment is accomplished. A series of simulations have been conducted using a two-wheeled mobile robot equipped with a laser scanner. It is shown that the proposed scheme can be applied relatively well.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2003.07b
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• pp.1227-1230
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• 2003

In this paper, we propose a pose estimation method using local map acquired from 2d laser range finder information. The proposed method uses extended kalman filter. The state equation is a navigation system equation of Nomad Super Scout II. The measurement equation is a map-based measurement equation using a SICK PLS 101-112 sensor. We describe a map consisting of geometric features such as plane, edge and corner. For pose estimation we scan external environments by laser rage finer. And then these data are fed to kalman filter to estimate robot pose and position. The proposed method enables very fast simultaneous map building and pose estimation.

• International Journal of Precision Engineering and Manufacturing
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• v.9 no.2
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• pp.69-74
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• 2008

Robotics and artificial intelligence are components of IT that involve networks, electrical and electronic engineering, and wireless communication. We consider an algorithm for efficient navigation by building a precise map in a multi-robot system under conditions of limited and unlimited communications. The basis of the navigation algorithm described in this paper is a wave algorithm, which is effective in obtaining an accurate map. Each robot in a multi-robot system has its own task such as building a map for its local position. By combining their data into a shared map, the robots can actively seek to verify their relative locations. Using shared maps, they coordinate their exploration strategies to maximize exploration efficiency. To prove the efficiency of the proposed technique, we compared the final results with the results in $Burgard^{8}$ and $Stachniss.^{9-10}$ All of the simulation comparisons, which are shown as graphs, were made in four different environments.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.101.1-101
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• 2001

This paper describes a grid mapping for a vision based mobile robot in uncertain indoor environment. The map building is a prerequisite for navigation of a mobile robot and the problem of feature correspondence across two images is well known to be of crucial Importance for vision-based mapping We use a stereo matching algorithm obtained by singular value decomposition of an appropriate correspondence strength matrix. This new correspondence strength means a correlation weight for some local measurements to quantify similarity between features. The visual range data from the reconstructed disparity image form an occupancy grid representation. The occupancy map is a grid-based map in which each cell has some value indicating the probability at that location ...

• International Journal of Control, Automation, and Systems
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• v.5 no.5
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• pp.577-583
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• 2007

Simultaneous Localization and Mapping (SLAM) is the process of building a map of an unknown environment and simultaneously localizing a robot relative to this map. SLAM is very important for the indoor navigation of a mobile robot and much research has been conducted on this subject. Although feature-based SLAM using an Extended Kalman Filter (EKF) is widely used, it has shortcomings in that the computational complexity grows in proportion to the square of the number of features. This prohibits EKF-SLAM from operating in real time and makes it unfeasible in large environments where many features exist. This paper presents an algorithm which reduces the computational complexity of EKF-SLAM by using topological information (TI) extracted through a thinning process. The global map can be divided into local areas using the nodes of a thinning-based topological map. SLAM is then performed in local instead of global areas. Experimental results for various environments show that the performance and efficiency of the proposed EKF-SLAM/TI scheme are excellent.

• Journal of Korean Society for Geospatial Information Science
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• v.15 no.4
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• pp.31-39
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• 2007

As the availability of images from airborne digital camera with high resolution is expanded, a lot of concern are shown about the production of orthoimage and digital map. This study presents the method of updating digital map using orthoimage from airborne digital camera image. Images were georectified using GPS surveying data. For the generation of orthoimage, Lidar DEM was used. The absolute positional accuracy of orthoimage was evaluated using GPS surveying data. And that of the building layer of digital map was estimated using the existed digital map at the scale of 1:1,000. The absolute positional accuracy of orthoimage was as followed: RMSE in X and Y were ${\pm}0.076m$ and ${\pm}0.294m$. The RMSE of the building layer were ${\pm}0.250m$ and ${\pm}0.210m$ in X and Y directions, respectively. The RMSE of the digital map using orthoimage from Aerial Digital Camera image fell within allowable error range established by NGII. Consequently, updating digital map using orthoimage from Aerial Digital Camera image can be applied to various fields including the construction of the framework data and the GIS of local government.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.468-471
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• 2003

The A＊ algorithm is usually used in game programming, mainly because it is fast in finding a optimal path to goal. In this paper. This algorithm was utilized for path finding, HIMM(Histogramic In-Motion Mapping) method is used in map-building. Map is updated continuously with range data sampled by PSD sensors From the map, A＊ algorithm finds a optimal path and sends subsequently the most suitable point to the Eyebot. A＊ algorithm has been tested on the Eyebot in various unknown maps of unknown and proved to work well. It could escape the local minimum, also.

• 한국신재생에너지학회:학술대회논문집
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• 2009.06a
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• pp.532-533
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• 2009

The national wind map of South Korea has been established as a core data to support national strategy building and promotion of wind energy dissemination. The national wind map has been made by numerical wind simulation with the spatial resolution of 1 km horizontal, 10m vertical and temporal resolution of 1 hour interval for 5 years period (2003-2007). Therefore, an application system linked with the national wind map named KIER-WindMap$^{TM}$ is being developed to be used by the government, local government, developers and researchers. We introduce the current status of the application system and the future development plans.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.9
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• pp.783-789
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• 2001

The purpose of this study is to build and to manage environment models with line segments from the sonar range data on obstacles in unknown and varied environments. The proposed method subsequently employs a two-stage data-transform process in order to extract environmental line segments from the range data on obstacles. In the first stage, the occupancy grid extracted from the range data is accumulated to a two-dimensional local histogram grid. In the second stage, a line histogram extracted from an local histogram gird is based on a Hough transform, and matching is a process of comparing each of the segments in the global line segments map against the line segments to detect similarity in overlap, orientation, and arrangement. Each of these tests is made by comparing one of the parameters in the segment representation. After the tests, new line segments are composed to the global line segments map. The proposed technique is illustrated by experiments in an indoor environment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.7
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• pp.1437-1443
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• 2009

This paper is presented an simultaneous localization and mapping (SLAM) algorithm using ultrasonic for robot and electric compass, encoder, and gyro. Generally, localization based upon electric compass, encoder, and gyro can be measured just local position in workspace. However, actual robot must need an information of the absolute position in workspace to perform its mission, Absolute position in workspace could be calculated using SLAM algorithm. To implement SLAM in this paper, a map is built using ultrasonic sensor and hierarchical map building method. And then, we the map will be transformed into a feature map. The absolute position could be calculated using the feature map and map mapping method. As a test bed, we designed and construct an autonomous robot and showed the experimental performance of the proposed SLAM algorithm based on feature map. Experimental result, we verified that robot can found all absolute position on experiments using proposed SLAM algorithm.