• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.8C
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• pp.814-821
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• 2009

In an ultrawide band (UWB) indoor wireless localization, time of arrival (TOA) parameter estimation techniques have some difficulties in acquiring a reasonable TOA estimate because of the clustered multipath components overlapping or random time intervals mainly due to non line-of-sight (NLOS) environment. In order to solve that problem and achieve an excellent UWB indoor wireless localization, we propose a UWB signal model and a robust TOA parameter estimation technique that has little effect on the clustered problems unlike the conventional technique. Through simulation studies, the validity of the proposed model and the TOA estimation technique are examined. The performance of estimation error is also analyzed.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.4
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• pp.445-450
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• 2009

This paper presents a new mobile robot localization method for indoor robot navigation. The method uses hexagon distributed color-coded patches on the ceiling and a camera is installed on the robot facing the ceiling to recognize these patches. The proposed "cell-coded map", with the use of only seven different kinds of color-coded landmarks distributed in hexagonal way, helps reduce the complexity of the landmark structure and the error of landmark recognition. This technique is applicable for navigation in an unlimited size of indoor space. The structure of the landmarks and the recognition method are introduced. And 2 rigid rules are also used to ensure the correctness of the recognition. Experimental results prove that the method is useful.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1731-1732
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• 2008

Recently, many researchers have developed various service robots, in which the position estimation and path following of mobile objects have been raised an important problem. We should know where a mobile robot so that there are many introduced localization and path following schemes. In this paper, we propose an efficient localization algorithm for the precise localization of a mobile robot with the glaser stream sensor. We use the glaser stream sensor for following a given path in indoor environments. Since the glaser stream sensor utilizes precise optical motion estimation technology, we can achieve high speed motion detection and high resolution. The experimental results show that the glaser stream sensor may be a good sensor for many indoor service robots.

• 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 Korean Institute of Building Construction Conference
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• 2023.05a
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• pp.205-206
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• 2023

This paper presents a new method for indoor localization track workers in construction sites. While GPS and NTRIP are effective for outdoor positioning, they are less accurate when used indoors. To address this issue, the proposed method utilizes Aruco markers to measure the distance between workers and the markers. By collecting data values, the location of each worker can be determined in real-time with high accuracy. This approach has the potential to enhance work efficiency and safety at construction sites, as it provides more precise indoor positioning compared to conventional methods, leading to improved work efficiency.

• Journal of Internet Computing and Services
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• v.11 no.1
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• pp.27-33
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• 2010

This paper applies Bayesian Markov inferred localization techniques for determining ZigBee mobile device's position. To evaluate its accuracy, we compare it with conventional technique, map-based localization. While the map-based localization technique referring to database of predefined locations and their RSSI data, the Bayesian Markov inferred localization is influenced by changes of time, direction and distance. All determinations are drawn from the estimation of Received Signal Strength (RSS) using ZigBee modules. Our results show the relationship between RSSI and distance in indoor ZigBee environment and higher localization accuracy of Bayesian Markov localization technique. We conclude that map-based localization is not suitable for flexible changes in indoors because of its predefined condition setup and lower accuracy comparing to distance-based Markov Chain inference localization system.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.20 no.2
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• pp.133-138
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• 2011

RFID system can be used to improve object recognition, map building and localization for robot area. A novel method of indoor navigation system for a mobile robot is proposed using RFID technology. The mobile robot With a RFID reader and antenna is able to find what obstacles are located where in circumstance and can build the map similar to indoor circumstance by combining RFID information and distance data obtained from sensors. Using the map obtained, the mobile robot can avoid obstacles and finally reach the desired goal by $A^*$ algorithm. 3D map which has the advantage of robot navigation and manipulation is able to be built using z dimension of products. The proposed robot navigation system is proved to apply for SLAM and path planning in unknown circumstance through numerous experiments.

• The KIPS Transactions:PartC
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• v.16C no.5
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• pp.645-652
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• 2009

Location-based service providing the customized information or service according to the user's location has attracted a lot of attention from the mobile communication industry. The service is realized by means of several building blocks, a localization scheme, service platform, application and service. The localization scheme figures out a moving target's position through measuring and processing a wireless signal. In this paper, we propose an adaptive localization scheme in an indoor localization system based on IEEE 802.15.4 standard. In order to enhance the localization accuracy, the proposed scheme selects the best reference points and adaptively reflects the changes of propagation environments of a moving target to approximate distances between the target and the reference points in RSS(Received Signal Strength) based localization system using triangulation. Through the implementation of the localization system, we verify the performance of the proposed scheme in terms of the localization accuracy.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.30 no.1
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• pp.87-95
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• 2016

Recently, the interests of the precise localization are rapidly increasing, which are linked to IoT(Internet of Things) sensors. The precise localization in indoor environment can be utilized in navigation, security, anti-collision, and various location based services etc. However, conventional positioning sensors, such as PIR, ultrasonic, microwave etc. are vulnerable to weather or insensitive to direction of subject movement or low precision performance. In this paper we implement a UWB-IR localization system for long distance and high-precision localization, which is not affected by temperature, light and weather. The proposed system was divided and designed by H/W, Antenna, S/W parts, each of which was designed based on an accurate analysis and simulation. As a result, we can implemented and verified UWB IR system with precise localization performance.

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.6
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• pp.57-65
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• 2018

Conventional RF signal-based indoor localization techniques such as BLE or Wi-Fi based fingerprinting method show considerable localization errors even in small-scale indoor environments due to unstable received signal strength(RSS) of RF signals. Therefore, it is difficult to apply the existing RF-based fingerprinting techniques to large-scale indoor environments such as airports and department stores. In this paper, instead of RF signal we use the geomagnetic sensor signal for indoor localization, whose signal strength is more stable than RF RSS. Although similar geomagnetic field values exist in indoor space, an object movement would experience a unique sequence of the geomagnetic field signals as the movement continues. We use a deep neural network model called the recurrent neural network (RNN), which is effective in recognizing time-varying sequences of sensor data, to track the user's location and movement path. To evaluate the performance of the proposed geomagnetic field based indoor positioning system (IPS), we constructed a magnetic field map for a campus testbed of about $94m{\times}26$ dimension and trained RNN using various potential movement paths and their location data extracted from the magnetic field map. By adjusting various hyperparameters, we could achieve an average localization error of 1.20 meters in the testbed.