• ETRI Journal
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• v.29 no.2
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• pp.189-200
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• 2007

This paper addresses the localization and navigation problem in the movement of service robots by using invisible two dimensional barcodes on the floor. Compared with other methods using natural or artificial landmarks, the proposed localization method has great advantages in cost and appearance since the location of the robot is perfectly known using the barcode information after mapping is finished. We also propose a navigation algorithm which uses a topological structure. For the topological information, we define nodes and edges which are suitable for indoor navigation, especially for large area having multiple rooms, many walls, and many static obstacles. The proposed algorithm also has the advantage that errors which occur in each node are mutually independent and can be compensated exactly after some navigation using barcodes. Simulation and experimental results were performed to verify the algorithm in the barcode environment, showing excellent performance results. After mapping, it is also possible to solve the kidnapped robot problem and to generate paths using topological information.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.5
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• pp.444-449
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• 2006

Path planning is a key element in navigation of a mobile robot. Several algorithms such as a gradient method have been successfully implemented so for. Although the gradient method can provide the global optimal path, it computes the navigation function over the whole environment at all times, which result in high computational cost. This paper proposes a high-speed path planning scheme, called a gradient method with topological information, in which the search space for computation of a navigation function can be remarkably reduced by exploiting the characteristics of the topological information reflecting the topology of the navigation path. The computing time of the gradient method with topological information can therefore be significantly decreased without losing the global optimality. This reduced path update period allows the mobile robot to find a collision-free path even in the dynamic environment.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.9 s.252
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• pp.1124-1133
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• 2006

This paper addresses the localization and navigation problem using invisible two dimensional barcodes on the floor. Compared with other methods using natural/artificial landmark, the proposed localization method has great advantages in cost and appearance, since the location of the robot is perfectly known using the barcode information after the mapping is finished. We also propose a navigation algorithm which uses the topological structure. For the topological information, we define nodes and edges which are suitable for indoor navigation, especially for large area having multiple rooms, many walls and many static obstacles. The proposed algorithm also has an advantage that errors occurred in each node are mutually independent and can be compensated exactly after some navigation using barcode. Simulation and experimental results. were performed to verify the algorithm in the barcode environment, and the result showed an excellent performance. After mapping, it is also possible to solve the kidnapped case and generate paths using topological information.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.6
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• pp.492-497
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• 2002

Map building is the process of modeling the robot's environment. The map is usually built based on a grid-based or topological approach, which has its own merits and demerits. These two methods, therefore, can be integrated to provide a better way of map building, which compensates for each other's drawbacks. In this paper, a method of building the topological map based on the occupancy grid map through a Voronoi diagram is presented and verified by various simulations. This Voronoi diagram is made by using a labeled Voronoi diagram scheme which is suitable for the occupancy grid maps. It is shown that the Proposed method is efficient and simple fur building a topological map. The simple path-planning problem is simulated and experimented verify validity of the proposed approach.

• 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.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2450-2452
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• 2004

Map building is essential to a mobile robot navigation system. Localization and path planning methods depend on map building strategies. A topological map is commonly constructed using the GVG(Generalized Voronoi Graph). The advantage of the GVG based topological map is compactness. But the GVG method have many difficulties because it consists of collision-free path. In this paper, we proposed an extended map building method, the AVG (Areal Voronoi Graph) based topological map. The AVG based topological map consists of collision-free area. This feature can improve map building, localization and path planning performance.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.7
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• pp.693-699
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• 2006

An accurate and compact map is essential to an autonomous mobile robot system. For navigation, it is efficient to use an occupancy grid map because the environment is represented by probability distribution. But it is difficult to apply it to the large environment since it needs a large amount of memory proportional to the environment size. As an alternative, a topological map can be used to represent it in terms of the discrete nodes with edges connecting them. It is usually constructed by the Voronoi-like graphs, but in this paper the topological map is incrementally built based on the local grid map using the thinning algorithm. This algorithm can extract only meaningful topological information by using the C-obstacle concept in real-time and is robust to the environment change, because its underlying local grid map is constructed based on the Bayesian update formula. In this paper, the position probability is defined to evaluate the quantitative reliability of the end nodes of this thinning-based topological map (TTM). The global TTM can be constructed by merging each local TTM by matching the reliable end nodes determined by the position probability. It is shown that the proposed TTM can represent the environment accurately in real-time and it is readily extended to the global TTM.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.5
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• pp.319-333
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• 2018

As human beings spend more time indoors, and with the growing complexity of indoor spaces, more focus is given to indoor spatial applications and services. 3D topological networks are used for various spatial applications that involve navigation indoors such as emergency evacuation, indoor positioning, and visualization. Manually generating indoor network data is impractical and prone to errors, yet current methods in automation need expensive sensors or datasets that are difficult and expensive to obtain and process. In this research, a methodology for semi-automatically generating a 3D indoor topological model based on IndoorGML (Indoor Geographic Markup Language) is proposed. The concept of Shooting Point is defined to accommodate the usage of omnidirectional images in generating IndoorGML data. Omnidirectional images were captured at selected Shooting Points in the building using a fisheye camera lens and rotator and indoor spaces are then identified using image processing implemented in Python. Relative positions of spaces obtained from CAD (Computer-Assisted Drawing) were used to generate 3D node-relation graphs representing adjacency, connectivity, and accessibility in the study area. Subspacing is performed to more accurately depict large indoor spaces and actual pedestrian movement. Since the images provide very realistic visualization, the topological relationships were used to link them to produce an indoor virtual tour.

• The Journal of Korea Robotics Society
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• v.14 no.4
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• pp.293-300
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• 2019

In this paper, an Embedded solution for fast navigation and precise positioning of mobile robots by floor features is introduced. Most of navigation systems tend to require high-performance computing unit and high quality sensor data. They can produce high accuracy navigation systems but have limited application due to their high cost. The introduced navigation system is designed to be a low cost solution for a wide range of applications such as toys, mobile service robots and education. The key design idea of the system is a simple localization approach using line features of the floor and delayed localization strategy using topological map. It differs from typical navigation approaches which usually use Simultaneous Localization and Mapping (SLAM) technique with high latency localization. This navigation system is implemented on single board Raspberry Pi B+ computer which has 1.4 GHz processor and Redone mobile robot which has maximum speed of 1.1 m/s.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.6
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• pp.554-560
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• 2006

In this paper, we propose the topological map building method about unknown environment using the ultrasonic sensors. An ultrasonic sensor inherently has the range error due to the specular reflection. To decrease this error, we estimate the obstacle states(position and velocity) using the local minimum sensor values and Jacobian. Estimated states are used to avoid the obstacles and build the topological map similar to the type that human being memorizes an environment. When a mobile robot is faced with three problems(comer way, cross way and dead end), it senses the movable directions using FIS(Fuzzy Inference System). Among these directions, it can select the target direction using binary decision tree(Turn Side Selector). Proposed algorithm has been verified with three simulations and three implementations.