• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.4
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• pp.506-510
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• 2007

Digital Magnetic Compass(DMC) has a robust feature against interference in the indoor environment better than compass which is easily disturbed by electromagnetic sources or large ferromagnetic structures. Ultrasonic Sensors are cheap and can give relatively accurate range readings. So they ate used in Simultaneous Localization and Mapping(SLAM). In this paper, we study the Simultaneous Localization and Mapping(SLAM) of mobile robot in the indoor environment with Digital Magnetic Compass and Ultrasonic Sensors. Autonomous mobile robot is aware of robot's moving direction and position by the restricted data. Also robot must localize as quickly as possible. And in the moving of the mobile robot, the mobile robot must acquire a map of its environment. As application for the Simultaneous Localization and Mapping(SLAM) on the autonomous mobile robot system, robot can find the localization and the mapping and can solve the Kid Napping situation for itself. Especially, in the Kid Napping situation, autonomous mobile robot use Ultrasonic sensors and Digital Magnetic Compass(DMC)'s data for moving. The robot is aware of accurate location By using Digital Magnetic Compass(DMC).

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2007.11a
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• pp.281-282
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• 2007

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.05a
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• pp.45-46
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• 2012

• ICROS
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• v.15 no.2
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• pp.17-25
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• 2009

본 고에서는 이동로봇의 동시간 위치인식 및 지도작성(Simultaneous Localizaton and Mapping;SLAM) 기술에 대하여 다룬다. 이동로봇의 SLAM을 위하여, 로봇과 랜드마크의 상태를 상태공간 영역에서 같이 기술하는 방법과 센서로부터 입력된 정보를 이용하여 로봇이 상태를 추정하는 기법을 소개한다. 실제 로봇을 통한 예제를 통하여 로봇의 상태와 특징점을 동시에 추정하는 것을 보여준다.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.11a
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• pp.185-186
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• 2010

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.241-243
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• 2018

In this paper, in Simultaneous localization and mapping (SLAM), the robot acquire its map of environment while simultaneously localizing itself relative to the map. Cloud based SLAM, allows us to optimizing resource and data sharing like map of the environment, which allows us, as one of shared available online map. Doing so, unless we add or remove significant change in our environment, the essence of rebuilding new environmental map are omitted to new mobile robot added to the environment. As result, the requirement of additional sensor are curtailed.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.8
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• pp.745-751
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• 2008

This paper presents a framework for GPS/INS/Vision based navigation system of helicopters. GPS/INS coupled algorithm has weak points such as GPS blockage and jamming, while the helicopter is a speedy and high dynamical vehicle amenable to lose the GPS signal. In case of the vision sensor, it is not affected by signal jamming and also navigation error is not accumulated. So, we have implemented an GPS/INS/Vision aided navigation system providing the robust localization suitable for helicopters operating in various environments. The core algorithm is the vision based simultaneous localization and mapping (SLAM) technique. For the verification of the SLAM algorithm, we performed flight tests. From the tests, we confirm the developed system is robust enough under the GPS blockage. The system design, software algorithm, and flight test results are described.

• International Journal of Control, Automation, and Systems
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• v.6 no.1
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• pp.62-75
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• 2008

Vision-based simultaneous localization and map building using a single camera, while compelling in theory, have not until recently been considered extensive in the practical realm of the real world. In this paper, we propose a simple framework for the monocular SLAM of an indoor mobile robot using natural line features. Our focus in this paper is on presenting a novel approach for modeling the landmark before integration in monocular SLAM. We also discuss data association improvement in a particle filter approach by using the feature management scheme. In addition, we take constraints between features in the environment into account for reducing estimated errors and thereby improve performance. Our experimental results demonstrate the feasibility of the proposed SLAM algorithm in real-time.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.4
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• pp.301-306
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• 2016

This paper presents the results of a study for robust self-localization and indoor slam using external cameras (such as a CCTV) and odometry of mobile robot. First, a position of mobile robot was estimated by using maker and odometry. This data was then fused with camera data and odometry data using an extended kalman filter. Finally, indoor slam was realized by applying the proposed method. This was demonstrated in the actual experiment.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.250-252
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• 2018

본 논문은 4차 산업 혁명 핵심 기술인 자율주행에 대하여 기술하였으며 그 중 Hector Slam을 사용 하였다. Hector slam 같은 경우 RAM이 4G 이상 되어야 제대로 동작하지만 SBC(Odroid xu4) 같은 경우 RAM의 크기가 2G이므로 최적화할 필요성이 있다. SBC(Odroid xu4)에서도 사용 가능하도록 Hector slam 구현 최적화를 하였으며, 향후에 Aruco Marker를 이용하여 위치를 좀 더 섬세히 보정 해볼 것이며 또한 Aruco Marker의 ID를 통해 사물 인식을 하여 사람에게 사물에 대한 정보를 알려줌으로써 사람과 협업을 할 수 있는 로봇이 될 것이다.