• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.270-271
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• 2015

Recently, There is Increasing interest in the intelligent services using the Internet of Things indoor positioning technologies that enable the communication of information between the objects. In particular Applications and demand for the indoor location based services using smart devices has made active. An indoor location positioning technology for this purpose BLE (Bluetooth Low Energy) has been a lot of interest in technology increases. If iBeacon of BLE(Bluetooth Low Energy) is made available to provide a signal for the indoor location information measurement then reliability of Indoor location information is lowered by signal interference. In this paper, Proposes a technique for data acquisition method for obtaining reliable position information and reliable position information calculation method from signal information data of iBeacon.

• Journal of Korea Multimedia Society
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• v.22 no.7
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• pp.780-789
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• 2019

In this paper, we propose a new indoor localization method for indoor mobile robots using LiDAR. The indoor mobile robots operating in limited areas usually require high-precision localization to provide high level services. The performance of the widely used localization methods based on radio waves or computer vision are highly dependent on their usage environment. Therefore, the reproducibility of the localization is insufficient to provide high level services. To overcome this problem, we propose a new localization method based on the comparison between ceiling shape information obtained from LiDAR measurement and the blueprint. Specifically, the method includes a reliable segmentation method to classify point clouds into connected planes, an effective comparison method to estimate position by matching 3D point clouds and 2D blueprint information. Since the ceiling shape information is rarely changed, the proposed localization method is robust to its usage environment. Simulation results prove that the position error of the proposed localization method is less than 10 cm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4606-4623
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• 2019

Nowadays modern cities develop in a very rapid speed. Buildings become larger than ever and the interior structures of the buildings are even more complex. This drives a high demand for precise and accurate indoor navigation systems. Although the existing commercially available 2D indoor navigation system can help users quickly find the best path to their destination, it does not intuitively guide users to their destination. In contrast, an indoor navigation system combined with augmented reality technology can efficiently guide the user to the destination in real time. Such practical applications still have various problems like position accuracy, position drift, and calculation delay, which causes errors in the navigation route and result in navigation failure. During the navigation process, the large computation load and frequent correction of the displayed paths can be a huge burden for the terminal device. Therefore, the navigation algorithm and navigation logic need to be improved in the practical applications. This paper proposes an improved navigation algorithm and navigation logic to solve the problems, creating a more accurate and effective augmented reality indoor navigation system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1200-1215
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• 2023

With the development of IoT and artificial intelligence, location-based services are getting more and more attention. For solving the current problem that indoor positioning error is large and generalization is poor, this paper proposes a Model Stacking Algorithm for Indoor Positioning System using WiFi fingerprinting. Firstly, we adopt a model stacking method based on Bayesian optimization to predict the location of indoor targets to improve indoor localization accuracy and model generalization. Secondly, Taking the predicted position based on model stacking as the observation value of particle filter, collaborative particle filter localization based on model stacking algorithm is realized. The experimental results show that the algorithm can control the position error within 2m, which is superior to KNN, GBDT, Xgboost, LightGBM, RF. The location accuracy of the fusion particle filter algorithm is improved by 31%, and the predicted trajectory is close to the real trajectory. The algorithm can also adapt to the application scenarios with fewer wireless access points.

• Journal of the Semiconductor & Display Technology
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• v.22 no.2
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• pp.50-55
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• 2023

Due to the unavailability of global positioning system (GPS) indoors, various indoor pedestrian positioning methods have been designed to estimate the position of the user using received signal strength (RSS) measurements from radio beacons, such as wireless fidelity (WiFi) access points and Bluetooth low energy (BLE) beacons. In indoor environments, radio-frequency (RF) signals are unpredictable and change over space and time because of multipath associated with reflection and refraction, shadow fading caused by obstacles, and interference among different devices using the same frequencies. Therefore, the outliers in the positional information obtained from the indoor positioning method based on RSS measurements occur often. For this reason, the performance of the positioning method can be degraded by the characteristics of the RF signal. To resolve this issue, a map-matching (MM) algorithm based on maximum probability (MP) estimation is applied to the indoor positioning method in this study. The MM algorithm locates the aberrant position of the user estimated by the positioning method within the limits of the adjacent pedestrian passages. Empirical experiments show that the positioning method can achieve higher positioning accuracy by leveraging the MM algorithm.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.91-94
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• 2001

In this paper, we proposed localization method of mobile robot using color landmark mounted on ceiling. This work is composed 2 parts : landmark recognition part which finds the position of multiple landmarks in image and identifies them and absolute position estimation part which estimates the location and orientation of mobile robot in indoor environment. In landmark recognition part, mobile robot detects artificial color landmarks using simple histogram intersection method in rg color space which is insensitive to the change of illumination. Then absolute position estimation part calculates relative position of the mobile robot to the detected landmarks. For the verification of proposed algorithm, ceiling-orientated camera was installed on a mobile robot and performance of localization was examined by designed artificial color landmarks. As the result of test, mobile robot could achieve the reliable landmark detection and accurately estimate the position of mobile robot in indoor environment.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.5
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• pp.419-428
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• 2011

This paper suggests a judgement method for an inclined plane before entrance of it and the detection of obstacle position. Main idea is started from the assumption that obstacle is always on the bottom plane, and corner appears at this position. The process to detect the obstacle consists of three steps. First the 3D data using stereo matching is acquired to detect an obstacle. Second a bottom plane is extracted by using limit condition. Last the obstacle position is found by using Harris corner detection. Obstacle position detection on an inclined plane was verified by outdoor and indoor experiment. In error analysis, it is confirmed that an average error of obstacle detection in outdoor was larger than the error in indoor but the error are within about 0.030 m. This method will be applied to unmanned vehicles to navigate under various environment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.7
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• pp.866-872
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• 2020

Indoor location-based services provide a service based on the distance between an object and a person. Recently, indoor location-based services are often implemented using inexpensive depth sensors such as Kinect. The depth sensor provides a function to measure the position of a person, but the position of an object must be acquired manually using a tool. To acquire a 3D position of an object, it requires 3D interaction, which is difficult to a general user. GUI(Graphical User Interface) is relatively easy to a general user but it is hard to gather a 3D position. This study proposes the Interactive LOcation Context Authoring Toolkit(ILOCAT), which enables a general user to easily acquire a 3D position of an object in real space using GUI. This paper describes the interaction design and implementation of ILOCAT.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.1
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• pp.31-38
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• 2017

As the structure of a construction, such as a building and a shopping mall, becomes more wide and complex, it is difficult for an user to find out a path to a desginated destination. However, since the existing navigation systems exploit the Global Positioning System in order to measure a current position, it is impossible to be used on the indoor environment. To solve this problem, we propose an indoor navigation system using beacon signal. The proposed system connects to a pre-installed beacon exploiting bluetooth on the user's smartphone. After receiving the data of the beacon, the system generates and displays a path based on the destination which the user put on and the current position measured by the beacon's data. The implemented indoor navigation system has the benefit to show the path to the destination easily at the complex indoor structure.

• Journal of Satellite, Information and Communications
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• v.11 no.4
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• pp.63-67
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• 2016

Recently, BLE beacons are widely used in indoor precision positioning systems because of their low battery consumption and low infrastructure cost. However, existing BLE beacon based indoor positioning algorithms are difficult to compensate for position errors due to the user's moving speed. Therefore, we proposed a position error correction algorithm that combines bounced cancellation and minimum distance maintenance algorithm with a positioning error correction method using direction vectors. Experimental results show that the proposed algorithm guarantees superior positioning performance than the existing indoor positioning algorithm and also improves the performance of position error compensation.