Large Indoor Spaces Research Articles

지각된 실내 공간장식이 기업 이미지에 미치는 영향에 관한 연구

This study is representative of decorations installed as flower decorations in cafes or offices with large and small indoor space decorations around us and indoor sculptures installed for visual promotion or meaning transmission in hotels and corporate lobbies. In order to examine how indoor space decorations including flower decorations affect not only cafes but also businesses the results were investigated through exploratory factor analysis based on the concept cases and theoretical research. First, through theoretical consideration, a questionnaire was created through operational definition for each factor, and the variable name was set as a questionnaire that combines the collected questionnaire contents into factor analysis, and a research hypothesis and research model were set. With the derived variables, the results were derived through descriptive statistics, reliability analysis, correlation analysis, and regression analysis. The hypothesis test results showed that only indoor sculptures affected customer satisfaction and indoor flower decorations and flower points did not affect corporate reliability, while customer satisfaction did not affect corporate reliability. The limitation of this study is that the survey was conducted as a subject who could not further subdivide the survey subjects, so I think it will be a more meaningful study if these research limitations are supplemented and developed in the future.

ULoc

A myriad of IoT applications, ranging from tracking assets in hospitals, logistics, and construction industries to indoor tracking in large indoor spaces, demand centimeter-accurate localization that is robust to blockages from hands, furniture, or other occlusions in the environment. With this need, in the recent past, Ultra Wide Band (UWB) based localization and tracking has become popular. Its popularity is driven by its proposed high bandwidth and protocol specifically designed for localization of specialized "tags". This high bandwidth of UWB provides a fine resolution of the time-of-travel of the signal that can be translated to the location of the tag with centimeter-grade accuracy in a controlled environment. Unfortunately, we find that high latency and high-power consumption of these time-of-travel methods are the major culprits which prevent such a system from deploying multiple tags in the environment. Thus, we developed ULoc, a scalable, low-power, and cm-accurate UWB localization and tracking system. In ULoc, we custom build a multi-antenna UWB anchor that enables azimuth and polar angle of arrival (henceforth shortened to '3D-AoA') measurements, with just the reception of a single packet from the tag. By combining multiple UWB anchors, ULoc can localize the tag in 3D space. The single-packet location estimation reduces the latency of the entire system by at least 3×, as compared with state of art multi-packet UWB localization protocols, making UWB based localization scalable. ULoc's design also reduces the power consumption per location estimate at the tag by 9×, as compared to state-of-art time-of-travel algorithms. We further develop a novel 3D-AoA based 3D localization that shows a stationary localization accuracy of 3.6 cm which is 1.8× better than the state-of-the-art two-way ranging (TWR) systems. We further developed a temporal tracking system that achieves a tracking accuracy of 10 cm in mobile conditions which is 4.3× better than the state-of-the-art TWR systems.

An RGB-D Camera Based Visual Positioning System for Assistive Navigation by a Robotic Navigation Aid

There are about 253 million people with visual impairment worldwide. Many of them use a white cane and/or a guide dog as the mobility tool for daily travel. Despite decades of efforts, electronic navigation aid that can replace white cane is still research in progress. In this paper, we propose an RGB-D camera based visual positioning system (VPS) for real-time localization of a robotic navigation aid (RNA) in an architectural floor plan for assistive navigation. The core of the system is the combination of a new 6-DOF depth-enhanced visual-inertial odometry (DVIO) method and a particle filter localization (PFL) method. DVIO estimates RNA's pose by using the data from an RGB-D camera and an inertial measurement unit (IMU). It extracts the floor plane from the camera's depth data and tightly couples the floor plane, the visual features (with and without depth data), and the IMU's inertial data in a graph optimization framework to estimate the device's 6-DOF pose. Due to the use of the floor plane and depth data from the RGB-D camera, DVIO has a better pose estimation accuracy than the conventional VIO method. To reduce the accumulated pose error of DVIO for navigation in a large indoor space, we developed the PFL method to locate RNA in the floor plan. PFL leverages geometric information of the architectural CAD drawing of an indoor space to further reduce the error of the DVIO-estimated pose. Based on VPS, an assistive navigation system is developed for the RNA prototype to assist a visually impaired person in navigating a large indoor space. Experimental results demonstrate that: 1) DVIO method achieves better pose estimation accuracy than the state-of-the-art VIO method and performs real-time pose estimation (18 Hz pose update rate) on a UP Board computer; 2) PFL reduces the DVIO-accrued pose error by 82.5% on average and allows for accurate wayfinding (endpoint position error ≤ 45 cm) in large indoor spaces.

Treatment with dry hydrogen peroxide accelerates the decay of severe acute syndrome coronavirus-2 on non-porous hard surfaces

BackgroundDisinfection of contaminated or potentially contaminated surfaces has become an integral part of the mitigation strategies for controlling coronavirus disease 2019. Whilst a broad range of disinfectants are effective in inactivating severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), application of disinfectants has a low throughput in areas that receive treatments. Disinfection of large surface areas often involves the use of reactive microbiocidal materials, including ultraviolet germicidal irradiation, chlorine dioxide, and hydrogen peroxide vapor. Albeit these methods are highly effective in inactivating SARS-CoV-2, the deployment of these approaches creates unacceptable health hazards and precludes the treatment of occupied indoor spaces using existing disinfection technologies. In this study, the feasibility of using dry hydrogen peroxide (DHP) in inactivating SARS-CoV-2 on contaminated surfaces in large indoor spaces was evaluated.MethodsGlass slides were inoculated with SARS-CoV-2 and treated with DHP between 5 and 25 ppb for up to 24 hours. Residual infectious virus samples were eluted from three replicates at each time point and titrated in African green monkey VeroE6 cells.ResultsIn comparison with the observed relatively high stability of SARS-CoV-2 on contaminated glass slides (control group), residual infectious titers of glass slides inoculated with SARS-CoV-2 were significantly reduced after receiving 120 minutes of DHP treatment.ConclusionsThe accelerated decay of SARS-CoV-2 on contaminated glass slides suggests that treatment with DHP can be an effective surface disinfection method for occupied indoor spaces

A Study on Building the Geospatial Data of the Firefighting Area by Applying the 3D Scanning Technology

The acceleration of the economic growth encourages the construction of high-rise and deep-underground buildings, which increase the possibility of the severe loss of life and property in the country in the event of an unexpected large-scale disaster, such as extreme weather. Therefore, recognizing the prior information of the fire scene in event of a fire disaster becomes an important issue for the prompt response; however, the current fire response system has a difficulty in the initial response because proactive collection of the indoor geospatial information on the building is limited in the event of a disaster. As a result, to present the plan for constructing the 3D geospatial data required for the initial response in a fire disaster scene, this study presented the construction case through 3D site scanning and data post-processing using a sample building. This study was conducted to ensure national safety in complex and large indoor spaces with substantial geospatial information of construction in the firefighting area by proposing both the linkage with the intelligent fire forecasting platform and the plan on introducing untact firefighting training as the plan of using the 3D geospatial data.

Generative multiview inpainting for object removal in large indoor spaces

As interest in image-based rendering increases, the need for multiview inpainting is emerging. Despite of rapid progresses in single-image inpainting based on deep learning approaches, they have no constraint in obtaining color consistency over multiple inpainted images. We target object removal in large-scale indoor spaces and propose a novel pipeline of multiview inpainting to achieve color consistency and boundary consistency in multiple images. The first step of the pipeline is to create color prior information on masks by coloring point clouds from multiple images and projecting the colored point clouds onto the image planes. Next, a generative inpainting network accepts a masked image, a color prior image, imperfect guideline, and two different masks as inputs and yields the refined guideline and inpainted image as outputs. The color prior and guideline input ensure color and boundary consistencies across multiple images. We validate our pipeline on real indoor data sets quantitatively using consistency distance and similarity distance, metrics we defined for comparing results of multiview inpainting and qualitatively.

Post COVID-19 HVAC System for Sustainable Virus Free Clean Air

Due to the globally occurring recurring waves of COVID-19, a major part of which is taking place through droplet and aerosolized mode of transmission within the indoor environment, it is of utmost importance to design new mode of indoor ventilation system which should be energy economic in one hand and should be capable of preventing indoor transmission of COVID-19 on the other hand. A novel concept of such post-COVID-19 HVAC system to provide virus free clean air for indoor environment in sustainable way is presented in this article. The concept is developed based on the published evidence from the literature. The proposed HVAC system consists of several integral components, such as, ultraviolet germicidal irradiation- and hydroxyl radical-based air purifier unit, MOFs- and nanowire-based air filtration unit and MOFs- and encapsulated phase change material-based hybrid desiccant air conditioning unit. Concept of personalized space ventilation is also proposed to ensure proper isolation between the building occupants. The proposed system is expected to be energy efficient in providing ventilation and thermal comfort for in large indoor spaces, e.g. office floors, restaurants, and patient isolation rooms in health care facilities.Funding Information: This work was supported by JST-CREST Grant No. JPMJCR17I3, JapanDeclaration of Interests: The authors declare no conflict of interest.

A Recurrent Deep Network for Estimating the Pose of Real Indoor Images from Synthetic Image Sequences.

Recently, deep convolutional neural networks (CNN) have become popular for indoor visual localisation, where the networks learn to regress the camera pose from images directly. However, these approaches perform a 3D image-based reconstruction of the indoor spaces beforehand to determine camera poses, which is a challenge for large indoor spaces. Synthetic images derived from 3D indoor models have been used to eliminate the requirement of 3D reconstruction. A limitation of the approach is the low accuracy that occurs as a result of estimating the pose of each image frame independently. In this article, a visual localisation approach is proposed that exploits the spatio-temporal information from synthetic image sequences to improve localisation accuracy. A deep Bayesian recurrent CNN is fine-tuned using synthetic image sequences obtained from a building information model (BIM) to regress the pose of real image sequences. The results of the experiments indicate that the proposed approach estimates a smoother trajectory with smaller inter-frame error as compared to existing methods. The achievable accuracy with the proposed approach is 1.6 m, which is an improvement of approximately thirty per cent compared to the existing approaches. A Keras implementation can be found in our Github repository.

Distance Estimation Based on Deep Convolutional Neural Network Using Ultra-Wideband Signals

Recently, high accuracy localization technique is required to provide indoor location services. The purpose of this paper is to propose a distance estimation technique based on deep convolutional neural network (DCNN) for indoor environments. Among distance estimation techniques based on wireless communication signals, the use of ultra-wideband (UWB) signals has the advantage of high accuracy in the time domain. The proposed distance estimation method uses UWB signals and proposes a new DCNN-based distance estimator. The superiority of the proposed method is confirmed through computer simulation. Widely used conventional distance estimators are based on the power threshold. The threshold is determined by signal to noise ratio (SNR) of the received signal. The arrival time of the received signal that exceeds the threshold is considered as the time-of-arrival (ToA) and the distance between transmitter and receiver is obtained from the ToA. On the other hand, the proposed distance estimator requires only the received signal without SNR estimation, which make the proposed technique simpler to implement. According to computer simulation, the conventional method is highly sensitive to SNR and distance. In contrast, the proposed method shows less than 2 m root mean square error (RMSE) performance in a wide range of SNR and the RMSE performance is not degraded in long distances. The proposed distance estimator shows excellent distance estimation performance at low SNR and long distance, so it can be applied to indoor localization system of large indoor space and can be used for precise location service.

Vivid: Augmenting Vision-Based Indoor Navigation System With Edge Computing

Indoor localization and navigation have a great potential of application, especially in large indoor spaces where people tend to get lost. The indoor localization problem is the fundamental of an indoor navigation system. Existing research and commercial efforts have leveraged wireless-based approaches to locate users in indoor environments. However, the predominant wireless-based approaches, such as WiFi and Bluetooth, are still not satisfactory, either not supporting commodity devices, or being vulnerable to environmental changes. These issues make them hard to deploy and maintain. In this paper, we present Vivid, a mobile device-friendly indoor localization and navigation system that leverages visual cues as the cornerstone of localization. By leveraging the computation power at the extreme internet edges, Vivid to a large extent overcomes the difficulties brought by resource-intensive image processing tasks. We propose a grid-based algorithm that transforms the feature map into a grid, with which finding the path between two positions can be easily obtained. We also leverage deep learning techniques to assist in automatic map maintenance to adapt to the visual changes and make the system more robust. With edge computing, user privacy is preserved since the visual data is mainly processed locally and detected dynamic objects are removed immediately without saving to databases. The evaluation results show that: i) our system easily outperforms the existing solutions on COTS devices in localization accuracy, yielding decimeter-level error; ii) our choice of the system architecture is scalable and optimal among the available ones; iii) the automatic map maintenance mechanism effectively ameliorates the localization robustness of the system.

Facility Standards and Layout Typology of Temporary Shelters in Response to Disasters - Focused on Size Standard and Block Layout of Public Facilities -

Local-level disasters continue to occur, which requires for long-term temporary housing operations. However, most researches on temporary housing planning have concentrated on individual units and space planning in large indoor spaces. The purpose of this paper is to develop the installation criteria and layout typology for public facilities. Based on the domestic system of temporary housing management, the types of public facilities were established by comparing the guidelines by international organizations with domestic standards. This research developed seven models of community-block layout based on grid distribution, community road, and center squares, and applied them to a virtual site that can accommodate the block size of domestic school grounds and the size standard of international organizations. The characteristics of each model were compared by three criteria including convenience of installation and distribution, privacy and community building. Each facility type can be utilized as a complementary to each other, according to a long-term disaster response scenario.

Designing indoor navigation interfaces on smartphones compatible with human information processing in an emergency evacuation scenario

ABSTRACTIn the discussion of the multiple aspects of underground urban facilities, emergency measures during disasters need to be appropriately planned to evacuate and relocate people instantly. People are prone to losing their way in large indoor spaces. Smartphones are equipped with multiple sensors and a multimedia interface; these features are now standard tools that be utilized as personal evacuation guides. Under the mental stress of emergency evacuation, people tend to make different perceptual judgments and decisions when processing the information on an interface. This study summarizes the current smartphone navigation system interfaces on the market as seven samples for the experiment. We chose the most advantageous information elements from the results to design two types of interfaces, namely continuous route view navigation and survey view navigation. Through experimental observation and think-aloud analysis of on-site simulation testing results, the proposed interface is both legible and readable and provides minimum message loading to guide users.

MODELLING UNCERTAINTY OF SINGLE IMAGE INDOOR LOCALISATION USING A 3D MODEL AND DEEP LEARNING

Abstract. Many current indoor localisation approaches need an initial location at the beginning of localisation. The existing visual approaches to indoor localisation perform a 3D reconstruction of the indoor spaces beforehand, for determining this initial location, which is challenging for large indoor spaces. In this research, we present a visual approach for indoor localisation that is eliminating the requirement of any image-based reconstruction of indoor spaces by using a 3D model. A deep Bayesian convolutional neural network is fine-tuned with synthetic images generated from a 3D model to estimate the camera pose of real images. The uncertainty of the estimated camera poses is modelled by sampling the outputs of the Bayesian network fine-tuned with synthetic images. The results of the experiments indicate that a localisation accuracy of 2 metres can be achieved using the proposed approach.

BIM-PoseNet: Indoor camera localisation using a 3D indoor model and deep learning from synthetic images

The ubiquity of cameras built in mobile devices has resulted in a renewed interest in image-based localisation in indoor environments where the global navigation satellite system (GNSS) signals are not available. Existing approaches for indoor localisation using images either require an initial location or need first to perform a 3D reconstruction of the whole environment using structure-from-motion (SfM) methods, which is challenging and time-consuming for large indoor spaces. In this paper, a visual localisation approach is proposed to eliminate the requirement of image-based reconstruction of the indoor environment by using a 3D indoor model. A deep convolutional neural network (DCNN) is fine-tuned using synthetic images obtained from the 3D indoor model to regress the camera pose. Results of the experiments indicate that the proposed approach can be used for indoor localisation in real-time with an accuracy of approximately 2 m.

Single- and Multiple-Access Point Indoor Localization for Millimeter-Wave Networks

Millimeter-wave (mm-wave) location systems not only provide accurate positioning for location-based services but can also help optimize network operations, for example, through location-driven beam steering and access point association. In this paper, we design and evaluate localization schemes that exploit the characteristics of mm-wave communication systems. We propose two range-free algorithms belonging to the broad classes of triangulation and angle difference of arrival. The schemes work both with multiple anchors and with as few as a single anchor, under the only assumption that the floor plan and the positions of the mm-wave access points are known. Moreover, they are designed to be lightweight so that even computationally-constrained devices can run them. We evaluate our proposed algorithms against two benchmark approaches based on fingerprinting and angles of arrival, respectively. Our results, obtained both by means of simulations and through measurements involving commercial 60-GHz mm-wave devices, show that sub-meter accuracy is achieved in most of the cases, even in the presence of only a single access point. The availability of multiple access points substantially improves the localization accuracy, especially for large indoor spaces.

How do people explore a large concourse in university campus? A computational analysis

Abstract Large concourses have been developed in shopping malls, bus and train terminals, concert, theatre halls, and other places where people stand or/and are in constant motion. This study aims to develop a novel method to analyze people's space use and navigation, demonstrate how people explore a large space, specifically a concourse in a university, and determine relationships of space with people's movements and activities. By collecting empirical data of spatial uses in a large concourse and implementing a method of spatial analysis based on spatial grids to analyze such data, this study seeks to verify how effectively people's movements and activities can be established in a large space and which features have significant effect in developing and managing a large space. A total of 865 activities in 265 days were collected and analyzed in a large space of a concourse in a university campus. By adopting a locational-based method of geographical analysis, the proposed method effectively illustrated relationships of people's movement, activities, and satisfaction in visual and quantitative values. As a result, spatial features were found to be significant elements in improving spatial satisfaction. Space environment performance, spatial form, user capacity, equipment adequacy, and equipment conditions were major spatial features considered for improving people's satisfaction in a large space. Highlights We proposed a new multi-layer-grid-based analysis method for a large indoor built environment. People's movements, activities, and satisfaction in a large indoor space were collected, traced, and analyzed. We quantified relationships among movements, activities, spatial features and satisfaction in a large indoor space. Assumption of causal symmetry between spatial features and people's behavior was tested with empirical data.

Room-Wide Wireless Charging and Load-Modulation Communication via Quasistatic Cavity Resonance

The rise of the Internet of Things (IoT) has led to a significant increase in the number of connected devices that stream data in our homes, offices and industrial spaces. However, as the number of these devices increases, the costs of actively maintaining and replacing batteries becomes prohibitive at scale. Recent work on Quasistatic Cavity Resonance (QSCR), offers the possibility of seamless wireless power transfer (WPT) to receivers placed anywhere inside large indoor spaces. This work aims to solve two unexplored and critical missing pieces needed to realize this vision of ubiquitous WPT. First, we demonstrate a full end-to-end QSCR-based WPT system that is capable of simultaneously charging multiple custom designed nodes nearly anywhere in the 4.9 m x 4.9 m x 2.3 m test room. Second, this work utilizes the WPT mechanism as a communication channel, where nodes communicate with a centralized reader and to each other via load modulation. Through analysis and experiments, the proposed system shows that 10 receiver nodes can be safely and efficiently wirelessly charged and the end node to end node communication rate can achieve from 1 kbps without occurring any errors, up to 5 kbps with 6% BER while the end node to central unit can achieve 10 kbps without occurring any errors.

Transparent Tunable Acoustic Absorber Membrane Using Inkjet-Printed PEDOT:PSS Thin-Film Compliant Electrodes.

Window glasses can block noise from outdoor, but they reverberate sound within a large indoor space. Microperforated glass absorbers have been developed to absorb sound over a fixed but narrow bandwidth. To tune the frequency spectrum of acoustic absorption, we developed a transparent tunable acoustic absorber based on microperforated dielectric elastomer actuator (MPDEA) and transparent compliant electrodes. Such transparent compliant electrodes were inkjet printed from Triton X-plasticized poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) ink, which shows improved wettability on the acrylate dielectric elastomer substrate. These transparent polymeric electrodes are softer with uptake of moisture while being self-clearable and durable. A single layer of MPDEA using two inkjet-printed electrodes is 78.64% clear, but the clarity of a two-layer MPDEA decreases to 61.8%. Among the two designs, the two-layer MPDEA exhibits a broader acoustic absorption bandwidth of 444 Hz for absorbing more than 80% of the sound energy. Inactivated resonant frequency of this MPDEA is 1170 Hz, whereas the 6 kV activation can reduce the resonant frequency for 15.2% by causing 9% hole-diameter contraction. This transparent tunable acoustic absorber can be fitted to window glass; its acoustic performance is better than that of translucent curtains.

Development of a high-endurance cleaning robot with scanning-based path planning and path correction

The cleaning of large-scale indoor public spaces is currently performed by workers driving cleaning machines. However, labor shortages are becoming more acute, resulting in human labor being substituted with robotics solutions for the cleaning of large public facilities (e.g., airport terminals, stations, and underground commercial streets). With the rapid development of sensing and automation technologies, various types of large-scale cleaning robots have been proposed. However, due to difficulty in precise indoor positioning, cleaning along a preplanned path is still challenging for robots. Therefore, this study proposes a cleaning robot that maps large indoor spaces using laser scanning and can avoid obstacles. The chassis of the robot has two independent primary wheels and two auxiliary wheels as power and support; direct current (DC) is employed to power the vacuum cleaner and the robot’s drive motor, to prevent power loss due to DC–alternating current conversion. An indoor facility undergoes three-dimensional laser scanning, and the cleaning space is then mapped through matrix graphics; subsequently, a path is planned using the boustrophedon method. The distance to the wall, measured by the laser rangefinder, is employed as a reference for the correction of the robot’s movement. The powerful vacuum cleaner design of the proposed cleaning robot was experimentally confirmed as having the ability to suction trash. In addition, the proposed robot was shown to clearly identify obstacles through laser scanning and successfully avoid them during the cleaning process to complete the cleaning task along the preplanned cleaning route.

An Indoor Positioning System Based on Static Objects in Large Indoor Scenes by Using Smartphone Cameras.

The demand for location-based services (LBS) in large indoor spaces, such as airports, shopping malls, museums and libraries, has been increasing in recent years. However, there is still no fully applicable solution for indoor positioning and navigation like Global Navigation Satellite System (GNSS) solutions in outdoor environments. Positioning in indoor scenes by using smartphone cameras has its own advantages: no additional needed infrastructure, low cost and a large potential market due to the popularity of smartphones, etc. However, existing methods or systems based on smartphone cameras and visual algorithms have their own limitations when implemented in relatively large indoor spaces. To deal with this problem, we designed an indoor positioning system to locate users in large indoor scenes. The system uses common static objects as references, e.g., doors and windows, to locate users. By using smartphone cameras, our proposed system is able to detect static objects in large indoor spaces and then calculate the smartphones’ position to locate users. The system integrates algorithms of deep learning and computer vision. Its cost is low because it does not require additional infrastructure. Experiments in an art museum with a complicated visual environment suggest that this method is able to achieve positioning accuracy within 1 m.