• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.36.5-36
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• 2001

Localization is one of the key problems in the navigation of autonomous mobile robots. The probabilistic Markov localization approaches offer a good mathematical framework to deal with the uncertainty of environment and sensor readings but their use for realtime applications is limited by their computational complexity. This paper aims to reduce the high computational cost associated with the probabilistic Markov localization algorithm. We propose a hybrid landmark-based localization method combining triangulation and probabilistic approaches, which can efficiently update position probability grid, while the probabilistic framework allows to make use of any available sensor data to refine robot´s belief about its current location. The simulation results show the effectiveness and robustness of the method.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.4
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• pp.381-390
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• 2010

This paper describes a procedure of the map-based localization for mobile robots by using a sensor fusion technique in structured environments. A combination of various sensors with different characteristics and limited sensibility has advantages in view of complementariness and cooperation to obtain better information on the environment. In this paper, for robust self-localization of a mobile robot with a monocular camera and a laser structured light sensor, environment information acquired from two sensors is combined and fused by a Bayesian sensor fusion technique based on the probabilistic reliability function of each sensor predefined through experiments. For the self-localization using the monocular vision, the robot utilizes image features consisting of vertical edge lines from input camera images, and they are used as natural landmark points in self-localization process. However, in case of using the laser structured light sensor, it utilizes geometrical features composed of corners and planes as natural landmark shapes during this process, which are extracted from range data at a constant height from the navigation floor. Although only each feature group of them is sometimes useful to localize mobile robots, all features from the two sensors are simultaneously used and fused in term of information for reliable localization under various environment conditions. To verify the advantage of using multi-sensor fusion, a series of experiments are performed, and experimental results are discussed in detail.

• Journal of the Korea Society of Computer and Information
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• v.10 no.3 s.35
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• pp.173-182
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• 2005

In this paper, we propose a landmark recognition method which is irrelevant to the camera viewpoint on the navigation for localization. Features in previous research is variable to camera viewpoint, therefore due to the wealth of information, extraction of visual landmarks for positioning is not an easy task. The proposed method in this paper, has the three following stages; first, extraction of features, second, learning and recognition, third, matching. In the feature extraction stage, we set the interest areas of the image. where we extract the corner points. And then, we extract features more accurate and resistant to noise through statistical analysis of a small eigenvalue. In learning and recognition stage, we form robust feature models by testing whether the feature model consisted of five corner points is an invariant feature irrelevant to viewpoint. In the matching stage, we reduce time complexity and find correspondence accurately by matching method using similarity evaluation function and Graham search method. In the experiments, we compare and analyse the proposed method with existing methods by using various indoor images to demonstrate the superiority of the proposed methods.

• Journal of Advanced Navigation Technology
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• v.24 no.5
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• pp.430-437
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• 2020

The proposed model implements a model that improves the face prediction rate and recognition rate through learning with an artificial neural network using face detection, landmark and face recognition algorithms. After landmarking in the face images of a specific person, the proposed model use the previously learned Caffe model to extract face detection and embedding vector 128D. The learning is learned by building machine learning algorithms such as support vector machine (SVM) and deep neural network (DNN). Face recognition is tested with a face image different from the learned figure using the learned model. As a result of the experiment, the result of learning with DNN rather than SVM showed better prediction rate and recognition rate. However, when the hidden layer of DNN is increased, the prediction rate increases but the recognition rate decreases. This is judged as overfitting caused by a small number of objects to be recognized. As a result of learning by adding a clear face image to the proposed model, it is confirmed that the result of high prediction rate and recognition rate can be obtained. This research will be able to obtain better recognition and prediction rates through effective deep learning establishment by utilizing more face image data.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.5
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• pp.577-582
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• 2004

This paper describes position estimation algorithm using neural network for the navigation of the vision-based Wheeled Mobile Robot (WMR) in a corridor with taking ceiling lamps as landmark. From images of a corridor the lamp's line on the ceiling in corridor has a specific slope to the lateral position of the WMR. The vanishing point produced by the lamp's line also has a specific position to the orientation of WMR. The ceiling lamps has a limited size and shape like a circle in image. Simple image processing algorithms are used to extract lamps from the corridor image. Then the lamp's line and vanishing point's position are defined and calculated at known position of WMR in a corridor To estimate the lateral position and orientation of WMR from an image, the relationship between the position of WMR and the features of ceiling lamps have to be defined. Data set between position of WMR and features of lamps are configured. Neural network are composed and teamed with data set. Back propagation algorithm(BPN) is used for learning. And it is applied in navigation of WMR in a corridor.

• Journal of Navigation and Port Research
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• v.39 no.4
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• pp.329-334
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• 2015

Currently, image of floating architecture has been positively improved based on the normalization of a domestic representative floating building 'Some Sevit'. Features of the floating architecture are as follows: special experience (29.9%)> landmark (27.6%)> enjoyment of marine culture (21.5%)> center for tourism and regional development (16.0%)> eco-friendly space solving global warming (4.8%). Floating building has a distinctive image and at the same time offers a unique spatial experience to the public. Therefore, space design marketing of floating building is a communication process to exchange its spatial identity and image between the local government and the public, the corporation and customers. It is essential for the effective space design marketing that the spatial demands should be reflected into its program such as commercial, cultural and marina facilities. The unification of project leader and operator is also important. The transformed conditions would help the construction market to be active in the future.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.9
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• pp.903-909
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• 2007

Mobile Robots are increasingly being used to perform tasks in unknown environment. The potential of robots to undertake such tasks lies in their ability to intelligently and efficiently search in an environment. To achieve autonomous mobile robot navigation, efficient path planner and accurate localization technique are the fundamental issues that should be addressed. This paper presents mobile robot localization using IRID(InfraRed IDentification) as artificial landmarks. IRID has highly deterministic characteristics, different from RFID. By putting several IRID emitters on the ceiling, the floor is divided into many different sectors and each sector is set to have a unique identification. Dead-reckoning provides the estimated robot configuration but the error becomes accumulated as the robot travels. IRID information tells the sector the robot is in, but the size of the uncertainty is too large if only the IRID information is used. This paper presents an algorithm which combines both the encoder and the IRID information so that the size of the uncertainty becomes smaller. It also introduces a framework which can be used with other types of the artificial landmarks. The characteristics of the developed IRID and the proposed algorithm are verified from the simulation results and experiments.

• Journal of the Korea Institute of Military Science and Technology
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• v.15 no.2
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• pp.169-176
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• 2012

GPS(Global Positioning System) is widely used for the position estimation of an aerial vehicle. However, GPS may not be available due to hostile jamming or strategic reasons. A vision-based position estimation method can be effective if GPS does not work properly. In mountainous areas without any man-made landmark, a horizon is a good feature for estimating the position of an aerial vehicle. In this paper, we present a new method to estimate the position of the aerial vehicle equipped with a forward-looking infrared camera. It is assumed that INS(Inertial Navigation System) provides the attitudes of an aerial vehicle and a camera. The horizon extracted from an infrared image is compared with horizon models generated from DEM(Digital Elevation Map). Because of a narrow field of view of the camera, two images with a different camera view are utilized to estimate a position. The algorithm is tested using real infrared images acquired on the ground. The experimental results show that the method can be used for estimating the position of an aerial vehicle.

• Journal of the Korean Geographical Society
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• v.45 no.1
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• pp.144-164
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• 2010

This study aims to reveal path selection strategies and individual differences in a navigation task. Two experiments were presented that studied human route planning performance as well as the cognitive strategies and processes involved. For the outdoor task, university students were asked to select a route based on the instruction, i.e. to find the best route from the campus main gate to the Education Building for conference visitors by locating eight signposts. Results indicate (1) that locations of signposts were selected preferably at decision points where the traveler needs to make a choice and starting/ending points of the navigation task and (2) a variety of route planning strategies considering efficiency goal (e.g., the shortest path), environmental characteristics (e.g., fewest turns), and aesthetic purpose (e.g., most scenic) were used. It is notable that some participants took into account more than one path by locating one or two signposts on an alternative route while others preferred a linear route connecting signposts between the start point and the destination. Prior to the main experiment, the same participants were asked to complete the same task inside the classroom to investigate changes in strategies between two tasks. Participants often tend to place signposts at more regular intervals for the indoor navigation task than the same task conducted outside.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.4
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• pp.187-194
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• 2016

When performing experiments in indoor and outdoor environment, we need a system that monitors a volunteer to prevent dangerous situations and efficiently manages the data in real time. We developed a guidance device for visually impaired person that guides the user to walk safely to the destination in the previous study. We set a POI (Point of Interest) of a specific location indoors and outdoors and tracks the user's position and navigate the walking path using artificial markers and ZigBee modules as landmark. In addition, we develop path finding algorithm to be used for navigation in the guidance device. In the test bed, the volunteers are exposed to dangerous situations and can be an accident due to malfunction of the device since they are visually impaired person or normal person wearing a eye patch. Therefore the device requires a system that remotely monitors the volunteer wearing guidance device and manages indoor or outdoor a lot of map data. In this paper, we introduce a managing system that monitors the volunteers remotely and handles map data efficiently. We implement a management system which can monitor the volunteer in order to prevent a hazardous situation and effectively manage large amounts of data. In addition, we verified the effectiveness of the proposed system through various experiments.