• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.545-554
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• 2018

As the map paradigm shifts from analog to digital, the LOD (Level Of Detail) of spatial information needs to be redefined. In this study, we propose 4- dimensional indoor LOD model which can be used in digital map environment. For this purpose, the limitation of the previous research is derived through study of related works, and based on this, four different LODs are defined such PLOD (Position accuracy LOD) based on position accuracy, GLOD (Geometric LOD) based on shape representation, CLOD (Complete LOD) based on generalization, and SLOD (Semantic LOD) based on theme accuracy. In addition, we describe the relationships among the four different LODs, and explain how to express the indoor LOD using the four different LODs and show examples. In the future, the case studies of indoor LOD adoption for various indoor services and the study of method for applying CLOD and SLOD to each feature should be performed to verify the feasibility and validity of proposed indoor LOD.

• Journal of the Korea Society of Computer and Information
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• v.17 no.6
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• pp.173-182
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• 2012

A recent advance in smart phones is increasing utilization of location information. Existing positioning system was using GPS location for positioning. However, the GPS cannot be used indoors, if GPS location has an incorrectly problem. In order to solve indoor positioning problems of indoor location-based positioning techniques have been investigated. There are a variety of techniques based on indoor positioning techniques like as RFID, UWB, WLAN, etc. But WLAN location positioning techniques take advantage the bond in real life. WLAN indoor positioning techniques have a two kind of method that is centroid and fingerprint method. Among them, the fingerprint technique is commonly used because of the high accuracy. In order to use fingerprinting techniques make a WLAN signal map building that is need to lot of resource. In this paper, we try to solve this problem in an Indoor environment for WLAN-based fingerprint of a virtual building technique, which is proposed. Proposed technique is classified Cell environment in existed Indoor environment, all of fingerprint points are shown virtual grid map in each Cell. Its method can make fingerprint grid map very quickly using estimate virtual signal value. Also built signal value can take different value depending of the real estimate value. To solve this problem using a calibration technique for the Splite-tree is proposed. Through calibration technique that improves the accuracy for short period of time. It also is improved overall accuracy using predicted value of around position in cell.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.05a
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• pp.79-80
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• 2010

• Journal of Satellite, Information and Communications
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• v.8 no.4
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• pp.64-69
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• 2013

Recently, users who are interested in the service at indoor spaces is increasing. An indoor positioning system can minimize a range of positioning error using a variety of wireless communication infrastructure. Also, the system improves an indoor positioning accuracy by combining a mobile communication network. However, flexible positioning technologies regardless of an environment are insufficient. Therefore, this is time for a systematic study on an indoor positioning system business model. This paper classify differences between an indoor positioning system technology and outdoor positioning system technology. And we research a construction and application of the indoor positioning system that is adapted a wireless communication system (Wi-Fi, Bluetooth, RFID, UWE, Fingerprint, etc.) in domestic and foreign. We present a successful model of indoor positioning system and the development for future systems.

• KIISE Transactions on Computing Practices
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• v.23 no.2
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• pp.110-115
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• 2017

We propose a new indoor positioning algorithm named Cut-off algorithm. This algorithm cuts off candidates of beacons and reference points in advance, before looking for K neighbor reference points which are guessed to be closest to the user's actual location. The algorithm consists of two phases: off-line phase, and on-line phase. In the off-line phase, RSSI and UUID data from beacons are gathered at reference points placed in the indoor environment, and construct a fingerprint map of the data. In the on-line phase, the map is reduced to a smaller one according to the RSSI data of beacons received from the user's device. The nearest K reference points are selected using the reduced map, which are used for estimating user's location. In both phases, relative ranks of the peak signals received from each beacon are used, which smoothen the fluctuations of the signals. The algorithm is shown to be more efficient in terms of accuracy and estimating time.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.4
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• pp.109-117
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• 2010

With the advent of new technologies such as HSDPA, WiBro(Wireless Broadband) and personal devices, we can access various contents and services anytime and anywhere. A location based service(LBS) is essential for providing personalized services with individual location information in ubiquitous computing environment. In this paper, we propose mapping algorithm for error compensation of indoor localization system. Also we explain filter and indoor localization system. we have developed mapping algorithms composed of a map recognition method and a position compensation method. The map recognition method achieves physical space recognition and map element relation extraction. We improved the accuracy of position searching. In addition, we reduced position errors using a dynamic scale factor.

• Nuclear Engineering and Technology
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• v.55 no.8
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• pp.3030-3038
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• 2023

Nowadays, mobile robots have been used to search for uncontrolled radioactive source in indoor environments to avoid radiation exposure for technicians. However, in the indoor environments, especially in the presence of obstacles, how to make the robots with limited sensing capabilities automatically search for the radioactive source remains a major challenge. Also, the source search efficiency of robots needs to be further improved to meet practical scenarios such as limited exploration time. This paper proposes an automatic source search strategy, abbreviated as ACA: the location of source is estimated by a convolutional neural network (CNN), and the path is planned by the A-star algorithm. First, the search area is represented as an occupancy grid map. Then, the radiation dose distribution of the radioactive source in the occupancy grid map is obtained by Monte Carlo (MC) method simulation, and multiple sets of radiation data are collected through the eight neighborhood self-avoiding random walk (ENSAW) algorithm as the radiation data set. Further, the radiation data set is fed into the designed CNN architecture to train the network model in advance. When the searcher enters the search area where the radioactive source exists, the location of source is estimated by the network model and the search path is planned by the A-star algorithm, and this process is iterated continuously until the searcher reaches the location of radioactive source. The experimental results show that the average number of radiometric measurements and the average number of moving steps of the ACA algorithm are only 2.1% and 33.2% of those of the gradient search (GS) algorithm in the indoor environment without obstacles. In the indoor environment shielded by concrete walls, the GS algorithm fails to search for the source, while the ACA algorithm successfully searches for the source with fewer moving steps and sparse radiometric data.

• Journal of Advanced Marine Engineering and Technology
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• v.40 no.1
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• pp.62-68
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• 2016

The fingerprint, which is one of the methods of indoor localization using WiFi, has been frequently studied because of its ability to be implemented via wireless access points. This method has low positioning resolution and high computational complexity compared to other methods, caused by its dependence of reference points in the radio map. In order to compensate for these problems, this paper presents a radio map designed algorithm based on the log-distance path loss model fusing a WiFi and BLE fingerprint. The proposed algorithm designs a radio map with variable values using the log-distance path loss model and reduces distance errors using a median filter. The experimental results of the proposed algorithm, compared with existing fingerprinting methods, show that the accuracy of positioning improved by from 2.747 m to 2.112 m, and the computational complexity reduced by a minimum of 33% according to the access points.

• The Journal of Korea Robotics Society
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• v.9 no.2
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• pp.96-103
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• 2014

Global localization is one of the essential issues for mobile robot navigation. In this study, an indoor global localization method is proposed which uses a Kinect sensor and a monocular upward-looking camera. The proposed method generates an environment map which consists of a grid map, a ceiling feature map from the upward-looking camera, and a spatial feature map obtained from the Kinect sensor. The method selects robot pose candidates using the spatial feature map and updates sample poses by particle filter based on the grid map. Localization success is determined by calculating the matching error from the ceiling feature map. In various experiments, the proposed method achieved a position accuracy of 0.12m and a position update speed of 10.4s, which is robust enough for real-world applications.

• Journal of information and communication convergence engineering
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• v.14 no.4
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• pp.207-214
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• 2016

In this paper, we propose a radial reference map-based location fingerprinting technique with constant spacing from an access point (AP) to all reference points by considering the minimum dynamic range of the received signal strength indicator (RSSI) obtained through an experiment conducted in an indoor environment. Because the minimum dynamic range, 12 dBm, of the RSSI appeared every 20 cm during the training stage, a cell spacing of 80 cm was applied. Furthermore, by considering the minimum dynamic range of an RSSI in the location estimation stage, when an RSSI exceeding the cumulative average by ${\pm}6dBm$ was received, a previously estimated location was provided. We also compared the location estimation accuracy of the proposed method with that of a conventional fingerprinting technique that uses a grid reference map, and found that the average location estimation accuracy of the conventional method was 21.8%, whereas that of the proposed technique was 90.9%.