• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.141-144
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• 1997

This paper presents a method of navigation sign recognition in indoor environments using a fuzzy inference for an autonomous mobile robot. In order to adapt to image deformation of a navigation sign resulted from variations of view-points and distances, a multi-labeled template matching(MLTM) method and a dynamic area search method(DASM) are proposed. The DASM is proposed to detect correct feature points among incorrect feature points. Finally sugeno-style fuzzy inference are adopted for recognizing the navigation sign.

• Journal of Advanced Navigation Technology
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• v.18 no.2
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• pp.134-141
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• 2014

Mobile robots use various sensors for navigation such as wheel encoder, vision sensor, sonar, and laser sensors. Dead reckoning is used with wheel encoder, resulting in the accumulation of positioning errors. For that reason wheel encoder can not be used alone. Too much information of vision sensors leads to an increase in the number of features and complexity of perception scheme. Also Sonar sensor is not suitable for positioning because of its poor accuracy. On the other hand, laser sensor provides accurate distance information relatively. In this paper we propose to extract the angular information from the distance information of laser range finder and use the Kalman filter that match the heading and distance of the laser range finder and those of wheel encoder. For laser scanner with one feature point error may increase much when the feature point is variant or jumping to a new feature point. To solve the problem, we propose to use two feature points and show that the positioning error can be reduced much.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.51-55
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• 1997

This study is devote to developing navigation filter for detecting sensor failure in multi-sensor navigation system. In multi-sensor navigation system, Kalman filter is generally used to fuse data of each sensors. Sensor failure is fatal in case that the sensor is used as external measurement of Kalman filter therefore detection and recovery of sensor failure is one the important feature of navigation filter. Generally each sensors have its specific feature in measuring navigational information. Fuzzy theory is proposed to detect external sensor failure and provide valid external measurement to Kalman filter avoiding filter divergence and instability. This idea is applied to Autonomous Underwater Vehicle(AUV) which has two navigation sensor i. e self contained inertial sensor and acoustic external sensor. 2 dimensional simulation result shows acceptable failure detection and recovery

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2016.05a
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• pp.314-316
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• 2016

본 연구에서는 IHO에서 발표한 S-100 표준에 맞춰 Feature Catalogue Builder의 고도화와 S-100 표준 등록소를 개발하였다. 기존의 S-57 기반에서는 Object, Attribute와 도메인의 효율적인 관리가 어려웠으나, S-100 기반의 시스템에서는 새로운 Item의 제안과 검토, 승인 절차 등이 표준화가 되어있어 보다 효과적인 관리가 가능할 것으로 예측된다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2016.05a
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• pp.317-318
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• 2016

국제수로기구(IHO)는 수로분야의 다양한 데이터와 서비스 표준개발을 위한 프레임워크인 S-100 표준을 개발 하였으며, 이를 기반으로 차세대 전자해도 표준을 포함하는 제품 및 서비스 표준을 개발하고 있다. S-100 기반 제품표준을 개발하기 위해서는 S-100 표준에 부합하는 Feature Catalogue(FC)가 포함되어야 하며, 이를 지원하기 위해 S-100 Feature Catalogue Builder(FCB)가 개발 되었다. S-100 FCB를 이용하여, FC제작 시 Feature를 정의하기 위해서는 Attribute를 매번 입력 하게 되는데, 공통 Attribute를 입력함에 있어서의 입력 오류와 장시간의 제작 시간이 소요된다는 문제점이 발생한다. 이러한 문제점은 현 S-100 FCB가 객체지향 개념이 고려되지 못한 것으로 판단하고, 본 연구에서는 OOP(Object-Oriented Programming) 기반의 S-100 FCB 개발 방안을 연구하였다. 이를 통해 중복되는 정보의 입력을 줄이고 제작 시간을 단축 할 수 있을 것으로 예상된다.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.98.4-98
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• 2002

$\textbullet$ Feature Detection $\textbullet$ Extracting Feature information $\textbullet$ Feature Map $\textbullet$ Feature Map Building $\textbullet$ Experimental Results

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.61-63
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• 2005

Retreiving indoor scene reference image from database using visual information is important issue in Robot Navigation. Scene matching problem in navigation robot is not easy because input image that is taken in navigation process is affinly distorted. We represent probabilistic framework for the feature matching between features in input image and features in database reference images to guarantee robust scene matching efficiency. By reconstructing probabilistic scene matching framework we get a higher precision than the existing feaure-feature matching scheme. To construct probabilistic framework we represent each image as Gaussian Mixture Model using Expectation Maximization algorithm using SIFT(Scale Invariant Feature Transform).

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.7
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• pp.673-683
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• 2010

This study investigates Terrain Aided Inertial Navigation(TAIN) which consists of Inertial Navigation System (INS) with the optical sensor for precise planetary landing. Image processing is conducted to extract the feature points between measured terrain data and on-board implemented terrain information. The navigation algorithm with Iterated Extended Kalman Filter(IEKF) can compensate for the navigation error, and provide precise navigation information compared to single INS. Simulation results are used to demonstrate the feasibility of integration to accomplish precise planetary landing. The proposed navigation approach can be implemented to the whole system coupled with guidance and control laws.

• Journal of the Korean Society of Industry Convergence
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• v.22 no.4
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• pp.427-433
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• 2019

This paper propose a fuzzy inference model for map building and navigation for a mobile robot with an active camera, which is intelligently navigating to the goal location in unknown environments using sensor fusion, based on situational command using an active camera sensor. Active cameras provide a mobile robot with the capability to estimate and track feature images over a hallway field of view. In this paper, instead of using "physical sensor fusion" method which generates the trajectory of a robot based upon the environment model and sensory data. Command fusion method is used to govern the robot navigation. The navigation strategy is based on the combination of fuzzy rules tuned for both goal-approach and obstacle-avoidance. To identify the environments, a command fusion technique is introduced, where the sensory data of active camera sensor for navigation experiments are fused into the identification process. Navigation performance improves on that achieved using fuzzy inference alone and shows significant advantages over command fusion techniques. Experimental evidences are provided, demonstrating that the proposed method can be reliably used over a wide range of relative positions between the active camera and the feature images.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.1
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• pp.35-42
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• 2012

In this study, a feature-points-tracking algorithm is suggested using a particle filter for vision based navigation system. By applying a dynamic model of the feature point, the tracking performance is increased in high dynamic condition, whereas a conventional KLT (Kanade-Lucas-Tomasi) cannot give a solution. Futhermore, the particle filter is introduced to cope with irregular characteristics of vision data. Post-processing of recorded vision data shows that the tracking performance of suggested algorithm is more robust than that of KLT in high dynamic condition.