• Journal of the Semiconductor & Display Technology
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• v.22 no.1
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• pp.6-10
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• 2023

In this paper, we proposed a software framework structure to apply ORB-SLAM, the most representative of SLAM algorithms, so that map creation and location estimation technology can be applied through tethered AR glasses. Since tethered AR glasses perform only the role of an input/output device, the processing of camera and sensor data and the generation of images to be displayed through the optical display module must be performed through the host. At this time, an Android-based mobile device is adopted as the host. Therefore, the major libraries required for the implementation of AR contents for AR glasses were hierarchically organized, and spatial recognition and location estimation functions using SLAM were verified.

• Journal of the Semiconductor & Display Technology
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• v.20 no.4
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• pp.96-101
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• 2021

The SLAM market is growing rapidly with advances in Machine Learning, Drones, Augmented Reality technologies. However, due to the absence of an open source-based SLAM library for developing AR content, most SLAM researchers are required to conduct their own research and development to customize SLAM. In this paper, we propose an opensource-based Mobile Markerless AR System by building our own pipeline based on Visual SLAM. To implement the Mobile AR System of this paper, it uses ORB-SLAM3 and Unity Engine and We experimented with running our system in a real environment and confirming it in the Unity Engine's Mobile Viewer. Through this experimentation, we can verify that the Unity Engine and the SLAM System are tightly integrated and communicate smoothly. Also, we expect to accelerate the growth of SLAM technology through this research.

• Journal of the Semiconductor & Display Technology
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• v.21 no.1
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• pp.136-141
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• 2022

In this paper, we designed, implemented, and verified the SLAM system that can be used on mobile devices. Mobile SLAM is composed of a stand-alone type that directly performs SLAM operation on a mobile device, and a mapping server type that additionally configures a mapping server based on FastAPI to perform SLAM operation on the server and transmits data for map visualization to a mobile device. The mobile SLAM system proposed in this paper is to mix the two types in order to make SLAM operation and map generation more efficient. The stand-alone type SLAM system was configured as an Android app by porting the OpenVSLAM library to the Unity engine, and the map generation and performance were evaluated on desktop PCs and mobile devices. The mobile SLAM system in this paper is an open source project, so it is expected to help develop AR contents based on SLAM in a mobile environment.

• Journal of KIBIM
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• v.10 no.1
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• pp.1-8
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• 2020

In managing building facilities, spatial information is the basic data for decision making. However, the method of acquiring spatial information is not easy. In many cases, the site and drawings are often different due to changes in facilities and time after construction. In this case, the site data should be scanned to obtain spatial information. The scan data actually contains spatial information, which is a great help in making space related decisions. However, to obtain scan data, an expensive LiDAR (Light Detection and Ranging) device must be purchased, and special software for processing data obtained from the device must be available.Recently, SLAM (Simultaneous localization and mapping), an advanced map generation technology, has been spreading in the field of robotics. Using SLAM, 3D spatial information can be obtained quickly in real time without a separate matching process. This study develops and tests whether SLAM technology can be used to obtain spatial information for facility management. This draws considerations for developing a SLAM device for real-time remote scanning for facility management. However, this study focuses on the system development method that acquires spatial information necessary for facility management through SLAM technology. To this end, we develop a prototype, analyze the pros and cons, and then suggest considerations for developing a SLAM system.

• 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.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.4
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• pp.687-694
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• 2022

In this paper, we proposed an autonomous vehicle platform that delivers goods to a designated destination based on the SLAM (Simultaneous Localization and Mapping) map generated indoors by applying the Visual SLAM technology. To generate a SLAM map indoors, a depth camera for SLAM map generation was installed on the top of a small autonomous vehicle platform, and a tracking camera was installed for accurate location estimation in the SLAM map. In addition, a convolutional neural network (CNN) was used to recognize the label of the destination, and the driving algorithm was applied to accurately arrive at the destination. A prototype of an indoor delivery autonomous vehicle was manufactured, and the accuracy of the SLAM map was verified and a destination label recognition experiment was performed through CNN. As a result, the suitability of the autonomous driving vehicle implemented by increasing the label recognition success rate for indoor delivery purposes was verified.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.12
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• pp.1021-1026
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• 2016

The graph-based SLAM (Simultaneous Localization and Mapping) approach has been gaining much attention in SLAM research recently thanks to its ability to provide better maps and full trajectory estimations when compared to the filtering-based SLAM approach. Even though graph-based SLAM requires batch processing causing it to be computationally heavy, recent advancements in optimization and computing power enable it to run fast enough to be used in real-time. However, data association problems still require large amount of computation when building a pose graph. For example, to find loop closures it is necessary to consider the whole history of the robot trajectory and sensor data within the confident range. As a pose graph grows, the number of candidates to be searched also grows. It makes searching the loop closures a bottleneck when solving the SLAM problem. Our approach to alleviate this bottleneck is to sample a limited number of pose nodes in which loop closures are searched. We propose a heuristic for sampling pose nodes that are most advantageous to closing loops by providing a way of ranking pose nodes in order of usefulness for closing loops.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.3
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• pp.312-320
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• 2020

3D scanning began in the field of manufacturing. In the construction field, a BIM (Building Information Modeling)-based 3D modeling environment was developed and used for the overall construction, such as factory prefabrication, structure construction inspection, plant facility, bridge, tunnel structure inspection using 3D scanning technology. LiDARs have higher accuracy and density than mobile scanners but require longer registration times and data processing. On the other hand, in interior building space management, relatively high accuracy is not needed, and the user can conveniently move with a mobile scan system. This study derives considerations for the development of Simultaneous Localization and Mapping (SLAM)-based Scan Backpack systems that move freely and support real-time point cloud registration. This paper proposes the mobile scan system, framework, and component structure to derive the considerations and improve scan productivity. Prototype development was carried out in two stages, SLAM and ScanBackpack, to derive the considerations and analyze the results.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.2
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• pp.28-35
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• 2009

Positioning technology which a part technology of Mobile Robot is an essential technology to locate the position of Robot and navigate to wanted position. The Robot that based on wheel drive uses Odometry position. technology. But when using Odometry positioning technology, it's hard to find out constant error value because a slip phenomenon occurs as the Robot runs. In this paper, we present the way to minimize positioning error by using Odometry and Inertial sensor. Also, the way to reduce error with Inertial sensor on SLAM using image will be shown, too.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.10
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• pp.1174-1180
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• 2011

Not all line or point features capable of being extracted by sonar sensors from cluttered home environments are useful for simultaneous localization and mapping (SLAM) due to their ambiguity because it is difficult to determine the correspondence of line or point features with previously registered feature. Confused line and point features in cluttered environments leads to poor SLAM performance. We introduce a sonar feature structure suitable for a cluttered environment and the extended Kalman filter (EKF)-based SLAM scheme. The reliable line feature is expressed by its end points and engaged togather in EKF SLAM to overcome the geometric limits and maintain the map consistency. Experimental results demonstrate the validity and robustness of the proposed method.