• Spatial Information Research
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• v.21 no.2
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• pp.1-9
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• 2013

Recently many researches have focused on indoor navigation system. An optimal indoor navigation method can help people to find a path in large and complex buildings easily. However, some indoor navigation algorithms only calculate approximate routes based on spatial topology analysis, while others only use indoor road networks. However, both of them use only one of the spatial topology or network information. In this paper, we present a navigation method based on topology analysis and room internal networks for indoor navigation path. FT-Indoornavi (Flexible Topology Analysis Indoornavi) calculate internal routes based on spatial topology and internal path networks to support length-dependent and running-time optimal routing, which adapt to complex indoor environment and can achieve a better performance in comparison of Elastic algorithm and iNav.

• Spatial Information Research
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• v.22 no.2
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• pp.31-44
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• 2014

According to the purpose of applications, the application program will utilize the most suitable data model and 3D modeling data would be generated based on the selected data model. In these reasons, there are various data sets to represent the same geographical features. The duplicated data sets bring serious problems in system interoperability and data compatibility issues, as well in finance issues of geo-spatial information industries. In order to overcome the problems, this study proposes a spatial data fusion method using topological relationships among spatial objects in the feature classes, called Topological Relation Model (TRM). The TRM is a spatial data fusion method implemented in application-level, which means that the geometric data generated by two different data models are used directly without any data exchange or conversion processes in an application system to provide indoor LBSs. The topological relationships are defined and described by the basic concepts of IndoorGML. After describing the concepts of TRM, experimental implementations of the proposed data fusion method in 3D GIS are presented. In the final section, the limitations of this study and further research are summarized.

• Journal of odor and indoor environment
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• v.17 no.4
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• pp.362-371
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• 2018

Indoor Volatile organic compounds (VOCs) are classified as known or possible toxicants and odorants. This study characterized VOC levels in 11 homes in an area in the capital of Seoul by using two different methods of VOCs sampling, which are the active sampling using a thermal sorption tube and the passive sampling using a diffusion sampler. When using the active sampling method, the total target VOC concentration ranged from 41.7 to $420.7{\mu}g/m^3$ (mean $230.4{\mu}g/m^3$ ; median $221.8{\mu}g/m^3$) during winter and 21.3 to $1,431.9{\mu}g/m^3$ (mean $340.1{\mu}g/m^3$; median $175.4{\mu}g/m^3$) during summer. When using the passive method, 29.6 to $257.5{\mu}g/m^3$ (mean $81.8{\mu}g/m^3$; median $49.4{\mu}g/m^3$) during winter and 1.2 to $5,131.1{\mu}g/m^3$ (mean $1,758.8{\mu}g/m^3$; median $1,375.1{\mu}g/m^3$) during summer. Forty-nine VOCs were quantified and toluene showed the highest concentration regardless of the season and the sampling method studied. The distribution of VOCs was relatively varied by using the active method. However, it showed a low correlation with indoor environmental factors such as room temperature, humidity and ventilation time. The correlation between indoor environmental factors and VOCs were relatively high in the passive method. In particular, these characteristics were confirmed by principal component analysis.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.5
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• pp.1881-1903
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• 2015

With the fast-developing of mobile terminals, positioning techniques based on fingerprinting method draws attention from many researchers even world famous companies. To conquer some shortcomings of the existing fingerprinting systems and further improve its performance, we propose a radio map building and updating technique, which is able to customize the spatial and temporal dependency of radio maps. The method includes indoor propagation and penetration modeling and the analysis of human traffic. Based on the combination of Ray-Tracing Algorithm, Finite-Different Time-Domain and Rough Set Theory, the approach of indoor propagation modeling accurately represents the spatial dependency of the radio map. In terms of temporal dependency, we specifically study the factor of moving people in the interest area. With measurement and statistics, the factor of human traffic is introduced as the temporal updating component. We improve our existing indoor positioning system with the proposed building and updating method, and compare the localization accuracy. The results show that the enhanced system can conquer the influence caused by moving people, and maintain the confidence probability stable during week, which enhance the actual availability and robustness of fingerprinting-based indoor positioning system.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.6
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• pp.366-371
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• 2005

For an autonomous mobile robot localization, it is very important for the robot to be able to recognize indoor environment and match a detected object to an object defined within a map developed either online or of offline. Given the map defining the locations of geometric beacons like wall and corner existing in the robot operation environment, this paper presents a stereo ultrasonic sensor based method practically applicable in recognizing the geometric beacons in real-time. The stereo ultrasonic sensor used in the experiment consists of an ultrasonic transmitter and two ultrasonic receivers placed symmetrically about the transmitter Experimental results are provided to demonstrate that the proposed method is more efficient in recognizing wall and coner than the conventional method of using multiple number of transmitter-receiver pairs.

• Journal of Platform Technology
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• v.12 no.1
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• pp.44-54
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• 2024

The existing indoor positioning methods using beacons or tags suffer from issues such as occasional undetection or increased errors due to noise. In this paper, we propose a method for determining the indoor position of a robot using the distance and, the angle between the direction of a target object whose position is known and the direction in which the robot views the target object from the front. The method proposed in this paper utilizes a depth camera to measure distance and calculate angles. Distance is measured using depth information captured by the camera, while angles are determined using images captured by the camera to determine the orientation of the target object. The proposed method calculates coordinate displacements using distance and angle. And then the method determines the position of the mobile robot using these displacements and the coordinates of the target object. To show the applicability of the proposed method for indoor positioning, we conducted experimental implementation and compared measured displacements. The results showed errors within 50mm, but considering the size of the mobile robot, it is judged that the method proposed in this paper can be sufficiently used for indoor positioning.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.658-664
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• 2024

The importance of 3D city models for sustainable urban development and management is underscored, but existing models often overlook indoor spaces and attribute information. This issue can be tackled with BIM models, though the conventional method requires accurate and extensive information, incurring considerable time and cost in data collection and processing. To overcome these limitations, this study proposes a method to automatically generate BIM models that include indoor spaces using street view images. The proposed method uses YOLOv5 to identify façade elements and DBSCAN to normalize façade layouts, facilitating the generation of detailed BIM models with a parametric algorithm. To validate the method, a case study of a building in Korea was conducted. The results showed that indoor spaces similar to the actual building were generated, with an error rate of object quantities between 8.46% and 9.03%. This study is anticipated to contribute to the efficient generation of 3D city models that incorporate indoor spaces.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.2
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• pp.119-124
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• 2013

Recent increasing demand on the indoor localization requires more advanced and hybrid technology. This paper proposes an application of the hybrid indoor localization method based on a position-coded pattern that can be used with other existing indoor localization techniques such as vision, beacon, or landmark technique. To reduce the pattern-recognition error rate, the error detection and correction algorithm was applied based on Hamming code. The indoor localization experiments based on the proposed algorithm were performed by using a QCIF-grade CMOS sensor and a position-coded pattern with an area of $1.7{\times}1.7mm^2$. The experiments have shown that the position recognition error ratio was less than 0.9 % with 0.4 mm localization accuracy. The results suggest that the proposed method could be feasibly applied for the localization of the indoor mobile service robots.

• Journal of the Korean Society of Safety
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• v.30 no.4
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• pp.142-150
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• 2015

A portable recoilless guided missile generates a strong back blast and impulsive noise at the nozzle when it launches. In the case of indoor operations, the hazard of the blast noise from a recoilless weapon increases due to limited indoor spaces. Also, the noise levels determine the operational feasibility of a weapon; therefore, it is important to predict the blast noise levels distribution in the indoor space in advance. In addition, computational fluid dynamics (CFD) method generally used for fluid related simulations, requires high computing cost and time to simulate the whole domains. The domain includes both blast wave region and large and various indoor space region. Therefore, an efficient method for predicting the far-field noise level within a short time should be developed. This paper describes an analysis model for predicting the indoor noise distributions by considering the shape effect of the building within a short time. A new developed blast wave model was implemented using the noise source. Additionally, noise reflections at the closed surfaces such as walls and noise transmissions at the opened surfaces such as windows and doors were considered in calculating the noise levels. The predicted noise levels were compared with the experimental data obtained from the indoor launch test to validate the reliability of program.