• Journal of Korea Game Society
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• v.19 no.5
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• pp.91-102
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• 2019

In this paper, we propose an improved method of plane tracking in developing AR games for smartphones using magnetic field sensor. The previous method based on ARCore is a VIO method using a mixture of SLAM and IMU of smartphones. The disadvantages of accelerometers and gyroscopes in IMUs cause errors in tracking the plane. We propose an improved method of planar tracking by adding the magnetic field sensor as well as the existing IMU sensors. Experimental results shows that our method reduces the error of the smartphone posture estimation.

• The Transactions of the Korea Information Processing Society
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• v.6 no.4
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• pp.1058-1070
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• 1999

In this paper, we propose a control model which can control the burst input messages of the BSC(Base Station controller) in mobile communication systems more efficiently and reliably, by dividing the input messages characteristically and using multiprocessor system. Using M/M/c/K queueing model, we briefly analyze proposed model to get characteristic parameters which are required to performance improvement. On the base of the results, we compare our proposed model with the conventional one by using SLAM II with regard to the following factors : the call blocking rate of the input message, the distribution of average queue length, the utilization of process controller(server), and the distribution of average waiting time in queue. In addition, we modified our model which has overload control function for burst input messages, and analyzed its performance.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.443-449
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• 2018

Acquiring indoor point cloud, using SLAM (Simultaneous Localization and Mapping) based mobile mapping system, is an element progress for development of as-build BIM (Building Information Model) for the maintenance of the building. In this research we proposed a simulation-based target geometry determination for extrinsic calibration of multiple 2D laser scanning mobile system. Four different types of calibration sites were designed: (1) circle type; (2) rectangle type; (3) double circle type; and (4) double rectangle type. Based on the measurement values obtained from each simulated calibration site geometry, least squares solution based extrinsic calibration was derived. As a result, the rectangle type geometry is most suitable for extrinsic calibration of this system. Also, correlation values between extrinsic calibration parameters were high, and calibration results were distinct according to the calibration sites.

• Journal of Sensor Science and Technology
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• v.32 no.2
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• pp.118-123
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• 2023

This study presents sensor system for autonomous mobile robot capable of floor-to-floor self-navigation. The robot was modified using the Turtlebot3 hardware platform and ROS2 (robot operating system 2). The robot utilized the Navigation2 package to estimate and calibrate the moving path acquiring a map with SLAM (simultaneous localization and mapping). For elevator boarding, ultrasonic sensor data and threshold distance are compared to determine whether the elevator door is open. The current floor information of the elevator is determined using image processing results of the ceiling-fixed camera capturing the elevator LCD (liquid crystal display)/LED (light emitting diode). To realize seamless communication at any spot in the building, the LoRa (long-range) communication module was installed on the self-navigating autonomous mobile robot to support the robot in deciding if the elevator door is open, when to get off the elevator, and how to reach at the destination.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.370-372
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• 2008

본 연구에서는 이동 로봇에 영역 센서를 장착하여 실내에서 주변환경을 인식하여 지도를 작성하는 방법을 제안한다. 이동 로봇이 미지의 환경에서 자율 주행하기 위해서는 로봇 환경에 대한 지도를 작성하면서 이 지도 상에서 로봇의 위치를 인식할 수 있어야한다. 지도 작성과 위치 인식을 동시에 수행하는 SLAM을 구현하기위한 준비단계로서 본 논문에서는 일정한 시간 간격으로 연속적인 센서 신호들로 부터 동일 특징을 추출하고 이들을 서로 일치시켜서 로봇 이동 및 센서 신호에 불확실성이 있는 경우에도 지도를 작성하는 방법을 연구한다. 실제로 레이저 영역 센서를 장착한 이동 로봇을 이용하여 실내에서 지도를 작성하는 실험을 통하여 제안된 방법의 성능을 검증한다.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1034-1039
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• 2005

SALM(Simultaneous localization and mapping) and AI(Artificial intelligence) have been active research areas in robotics for two decades. In particular, localization is one of the most important tasks in mobile robot research. Until now expensive sensors such as a laser sensor have been used for mobile robot localization. Currently, the proliferation of RFID technology is advancing rapidly, while RFID reader devices, antennas and tags are becoming increasingly smaller and cheaper. So, in this paper, the smart floor using passive RFID tags is proposed and, passive RFID tags are mainly used for identifying location of the mobile robot in the smart floor. We discuss a number of challenges related to this approach, such as tag distribution (density and structure), typing and clustering. In the smart floor using RFID tags, the localization error results from the sensing area of the RFID reader, because the reader just knows whether the tag is in the sensing range of the sensor and, until now, there is no study to estimate the heading of mobile robot using RFID tags. So, in this paper, two algorithms are suggested to. The Markov localization method is used to reduce the location(X,Y) error and the Kalman Filter method is used to estimate the heading($\theta$) of mobile robot. The algorithms which are based on Markov localization require high computing power, so we suggest fast Markov localization algorithm. Finally we applied these algorithms our personal robot CMR-P3. And we show the possibility of our probability approach using the cheap sensors such as odometers and RFID tags for mobile robot localization in the smart floor

• Journal of Drive and Control
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• v.19 no.4
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• pp.97-103
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• 2022

Low fertility rates and increased life expectancy further exacerbate the process of an aging society. This is also reflected in the gradual increase in the proportion of vulnerable groups in the social population. The demand for improved mobility among vulnerable groups such as the elderly or the disabled has greatly driven the growth of the electric-assisted mobility device market. However, such mobile devices generally require a certain operating capability, which limits the range of vulnerable groups who can use the device and increases the cost of learning. Therefore, autonomous driving technology needs to be introduced to make mobility easier for a wider range of vulnerable groups to meet their needs of work and leisure in different environments. This study uses mini PC Odyssey, Velodyne Lidar VLP-16, electronic device and Linux-based ROS program to realize the functions of working environment recognition, simultaneous localization, map generation and navigation of electric powered mobile devices for vulnerable groups. This autonomous driving mobility device is expected to be of great help to the vulnerable who lack the immediate response in dangerous situations.

• The Journal of Korea Robotics Society
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• v.9 no.3
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• pp.170-177
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• 2014

This paper proposes a novel upward-looking camera-based global localization using a ceiling image map. The ceiling images obtained through the SLAM process are integrated into the ceiling image map using a particle filter. Global localization is performed by matching the ceiling image map with the current ceiling image using SURF keypoint correspondences. The robot pose is then estimated by the coordinate transformation from the ceiling image map to the global coordinate system. A series of experiments show that the proposed method is robust in real environments.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1767-1768
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• 2007

This paper represents a map building system of Embedded Linux mobile robot. We propose a localization method which uses multiple sensors such as indoor GPS and encoder sensor for simultaneous map building system. In this paper we proposed a multiple sensor system for SLAM. For this, we developed a sensor based navigation algorithm and grid based map building algorithm under the Embedded Linux O.S. We proved this system's validity through field test

• Korean Journal of Remote Sensing
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• v.39 no.1
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• pp.111-127
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• 2023

In visual slam and 3D data modeling, the Iterative Closest Point method is a primary fundamental algorithm, and many technical fields have used this method. However, it relies on search methods that take a high search time. This paper solves this problem by applying an effective point cloud refinement method. And this paper also accelerates the point cloud registration process with an indexing scheme using the spatial decomposition method. Through some experiments, the results of this paper show that the proposed point cloud refinement method helped to produce better performance.