• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.240-251
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• 2016

This paper presents a real-time indoor location and pose estimation method that utilizes simple artificial markers and image analysis techniques for the purpose of warehouse automation. The conventional indoor localization methods cannot work robustly in warehouses where severe environmental changes usually occur due to the movement of stocked goods. To overcome this problem, the proposed framework places artificial markers having different interior pattern on the predefined position of the warehouse floor. The proposed algorithm obtains marker candidate regions from a captured image by a simple binarization and labeling procedure. Then it extracts maker interior pattern information from each candidate region in order to decide whether the candidate region is a true marker or not. The extracted interior pattern information and the outer boundary of the marker are used to estimate location and heading angle of the localization system. Experimental results show that the proposed localization method can provide high performance which is almost equivalent to that of the conventional method using an expensive LIDAR sensor and AMCL algorithm.

• Spatial Information Research
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• v.16 no.4
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• pp.455-465
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• 2008

Augmented reality (AR) has tremendous potential in visualizing geospatial information, especially on the actual physical scenes. However, to utilize augmented reality in mobile system, many researches have undergone with GPS or ubiquitous marker based approaches. Although there are several papers written with vision based markerless tracking, previous approaches provide fairly good results only in largely under "controlled environments." Localization and tracking of current position become more complex problem when it is used in indoor environments. Many proposed Radio Frequency (RF) based tracking and localization. However, it does cause deployment problems of large RF-based sensors and readers. In this paper, we present a noble markerless AR approach for indoor (possible outdoor, too) navigation system only using monoSLAM (Monocular Simultaneous Localization and Map building) algorithm to full-fill our grand effort to develop mobile seamless indoor/outdoor u-GIS system. The paper briefly explains the basic SLAM algorithm, then the implementation of our system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.6
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• pp.640-651
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• 2016

We propose an effective ToA-based localization method considering accuracy in indoor environments. The purpose of the localization system is to estimate the coordinates of the geographic location of target device. In indoor environments, accurately estimating the location of a target device is not easy due to various errors. The accuracy of wireless localization is influenced by NLOS errors. ToA-based localization measures the location of a target device using the distances between a mobile device and three or more base stations. However, each of the NLOS errors along a distance estimated from a target device to a base station is different because of dissimilar obstacles. To accurately estimate the target's location, an optimized localization process is needed in indoor environments. In this paper, effective ToA-based localization method process is proposed for improving accuracy in wireless sensor networks. Performance evaluations are presented, and the experimental localization system results are proved through comparisons of various localization methods with the proposed methods.

• KIISE Transactions on Computing Practices
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• v.23 no.4
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• pp.210-216
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• 2017

In the paper we propose and implement a new indoor localization system where the techniques of magnetic field based fingerprinting and pedestrian dead reckoning are combined. First, we determine a target's location by comparing acquired magnetic field values with a magnetic field map containing pre-collected field values at different locations and choosing the location having the closest value. As the target moves, we use pedestrian dead reckoning to estimate the expected moving path, reducing the maximum positioning error of the initial location. The system eliminates the problem of localization error accumulation in pedestrian dead reckoning with the help of the fingerprinting and does not require Wi-Fi AP infrastructure, enabling cost-effective localization solution.

• Journal of Engineering Education Research
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• v.13 no.5
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• pp.76-80
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• 2010

In this paper, the Location Compensation Mechanism using equivalent distance rate ($LCM_{edr}$) for localization system based on SDS-TWR (Symmetric Double-Sided Two-Way Ranging) in wireless sensor network is proposed. The performance of the mechanism is experimented in terms of two types of the localization tracking scenarios of indoor and outdoor environments in university campus. From the experimentations, the compensation ratio in the $LCM_{edr}$ is better than that in SDS-TWR about 90% in indoor/outdoor environments in scenario 1 but also is better than that of SDS-TWR about 91.7% in indoor environment and about 100% in outdoor environment in scenario 2 respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.5
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• pp.1317-1340
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• 2024

With the rapid development of information technology, people's demands on precise indoor positioning are increasing. Wireless sensor network, as the most commonly used indoor positioning sensor, performs a vital part for precise indoor positioning. However, in indoor positioning, obstacles and other uncontrollable factors make the localization precision not very accurate. Ultra-wide band (UWB) can achieve high precision centimeter-level positioning capability. Inertial navigation system (INS), which is a totally independent system of guidance, has high positioning accuracy. The combination of UWB and INS can not only decrease the impact of non-line-of-sight (NLOS) on localization, but also solve the accumulated error problem of inertial navigation system. In the paper, a fused UWB and INS positioning method is presented. The UWB data is firstly clustered using the Fuzzy C-means (FCM). And the Z hypothesis testing is proposed to determine whether there is a NLOS distance on a link where a beacon node is located. If there is, then the beacon node is removed, and conversely used to localize the mobile node using Least Squares localization. When the number of remaining beacon nodes is less than three, a robust extended Kalman filter with M-estimation would be utilized for localizing mobile nodes. The UWB is merged with the INS data by using the extended Kalman filter to acquire the final location estimate. Simulation and experimental results indicate that the proposed method has superior localization precision in comparison with the current algorithms.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.9
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• pp.925-930
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• 2008

Many sensor network applications require that each node's sensor data stream be annotated with its physical location in some coordinate system. Equipping GPS on every sensor node is often expensive and does not work in indoor deployments. Recently, cricket-based localization system is often used for indoor localization system. It is very important to know the exact position of beacons in cricket-based localization system for identifying moving sensor node's position. In this paper, a new method, Mobile Listener Detect Algorithm (MLD) which can automatically calculate the unknown newly installed beacons is proposed. For the verification of the feasibility of the proposed scheme, we have conducted several experiments.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.1
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• pp.41-48
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• 2014

This paper analyzes the Monte Carlo localization (MCL) method, which estimates the pose of an indoor mobile robot. A mobile robot must know where it is to navigate in an indoor environment. The MCL technique is one of the most influential and popular techniques for estimation of robot position and orientation using a particle filter. For the analysis, we perform experiments in an indoor environment with a differential drive robot and ultrasonic range sensor system. The analysis uses MATLAB for implementation of the MCL and investigates the effects of the control parameters on the MCL performance. The control parameters are the uncertainty of the motion model of the mobile robot and the noise level of the measurement model of the range sensor.

• The Journal of Korea Robotics Society
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• v.6 no.1
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• pp.58-68
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• 2011

This paper proposes a technique of indoor localization for mobile robot by so called indoor GPS and EKF. Basically the concept of indoor GPS is similar outdoor GPS, and the indoor GPS gets distances between Anchors and Tag by a ranging method of CSS and then estimates the coordinate by distances and known Anchor positions. After we performed performance test of indoor GPS system in ideal and multipath environment, we configured that the indoor GPS has internal error factors and external error factors. This paper handled a multipath problem belonging to external error factors. At first various direct physical method are introduced to fix the multipath problems, and as expected we got errors corrected considerably. And then the method of selective anchors for indoor GPS is applied. With these two level improvement of indoor GPS performance, EKF(Extended Kalman Filter) is applied to mobile robot in indoor environment. The usefulness of the proposed methods are shown by a series of experiments in a environment giving contaminated data by multipath.

• Journal of Power System Engineering
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• v.17 no.5
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• pp.100-111
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• 2013

Indoor real-time positioning for multiple targets is required to realize human-robot symbiosis. This study firstly presents positioning accuracy on an autonomous mobile robot controlled by 3-D coordinates that is obtained by a real-time indoor positioning system with spread spectrum (SS) ultrasonic signals communicated by code-division multiple access. Although many positioning systems have been investigated, the positioning system with the SS ultrasonic signals can measure identified multiple 3-D positions in every 70 ms with noise tolerance and error within 100 mm. This system is also robust to occlusion and environmental changes. However, thus far, the positioning errors in an autonomous mobile robot, controlled by these systems using the SS ultrasonic signals, have not been evaluated as an experimental study. Therefore, a positioning experiment for trajectory control is conducted using an autonomous mobile robot and our positioning system. The effectiveness of this positioning method for robot self-localization is shown, from this experiment, because the average control error between the target position and the robot's position at 29 mm is obtained.