Changes In Indoor Environment Research Articles

IndoorMCD: A Benchmark for Low-Cost Multi-Camera SLAM in Indoor Environments

Navigating mobile robots within home environments is essential for future applications, e.g. in household or within the field of elderly care. Therefore, these systems, equipped with multiple sensors, have to deal with changing environments. This work presents the IndoorMCD dataset that allows for benchmarking slam algorithms within static and changing indoor environments of various difficulties. The dataset provides synchronized and calibrated RGB-D images from a low-cost multi-camera setup, as well as additional imu data. Further, highly accurate ground truth movement data is provided. It is the first dataset that provides static and changing environments for a multi-camera setup. Evaluations with state-of-the-art slam algorithms show the dataset's applicability and also present limitations of current approaches. The dataset is made available in a structured format and a utility library with example scripts is provided.

RRIFLoc: Radio Robust Image Fingerprint Indoor Localization Algorithm Based on Deep Residual Networks

Indoor localization is one of the most exciting research areas due to the increasing demand for location-based services (LBSs) in indoor environments. The fingerprint positioning method of the received signal strength indicator (RSSI) is widely used in indoor localization due to its simple deployment and low cost. However, since the RSSI is affected by indoor environment changes and the heterogeneous nature of devices, it is easy to cause fingerprint drift and disappearance of fingerprint features, resulting in low accuracy and weak robustness of indoor localization. In this article, we propose a robust indoor localization method that is calibrated-free of Wi-Fi image fingerprints, called the radio robust image fingerprint localization (RRIFLoc) algorithm. First, the signal strength difference (SSD) fingerprint and RSSI kurtosis are derived from the RSSI fingerprint. SSD and kurtosis alleviate the low positioning accuracy and weak anti-interference caused by fingerprint drift and the disappearance of fingerprint features. Second, the fusion of the RSSI, SSD, and kurtosis is constructed into a radio robust image fingerprint (RRIF). Finally, we build the RRIFLoc model using the generated RRIF and the deep residual network for location estimation. According to experiments on a public dataset, our method reduces the average location estimation error by 56.87% compared to state-of-the-art indoor fingerprint localization methods.

Long-Term Localization Using Semantic Cues in Floor Plan Maps

Lifelong localization in a given map is an essential capability for autonomous service robots. In this letter, we consider the task of long-term localization in a changing indoor environment given sparse CAD floor plans. The commonly used pre-built maps from the robot sensors may increase the cost and time of deployment. Furthermore, their detailed nature requires that they are updated when significant changes occur. We address the difficulty of localization when the correspondence between the map and the observations is low due to the sparsity of the CAD map and the changing environment. To overcome both challenges, we propose to exploit semantic cues that are commonly present in human-oriented spaces. These semantic cues can be detected using RGB cameras by utilizing object detection, and are matched against an easy-to-update, abstract semantic map. The semantic information is integrated into a Monte Carlo localization framework using a particle filter that operates on 2D LiDAR scans and camera data. We provide a long-term localization solution and a semantic map format, for environments that undergo changes to their interior structure and detailed geometric maps are not available. We evaluate our localization framework on multiple challenging indoor scenarios in an office environment, taken weeks apart. The experiments suggest that our approach is robust to structural changes and can run on an onboard computer. We released the open source implementation of our approach written in C++ together with a ROS wrapper.

Influence of Indoor Environment Changes in Relaxation Space on Relaxation and Refreshment Effects of Occupants

Since proper break between office work is helpful in decreasing work-related stress and fatigue, a break space in office is also an important place to create healthy and productive workplace environment. However, there are relatively few studies focused on the break space compared to ones focused on work space. In this study, experiments were carried out to investigate the influence of the break space’s indoor environment on occupants’ psychological and physiological responses. To examine whether change in indoor environment during break positively affect occupants’ responses and productivity, lighting, odor and air movement conditions were intentionally changed before the end of the break. As for the psychological response, there was a possibility that it would be affected when the lighting and air movement were changed. As for brain waves, effects were also observed when lighting and air movement were changed. In terms of productivity, performance was better when the environment was changed than when the environment was left unchanged during breaks. The need for breaks was indicated because the worst results were obtained when the work was done continuously without a break in the first place. The best results were obtained when the environment was changed to generate an odor.

CSI-Based Human Activity Recognition With Graph Few-Shot Learning

Human activity recognition (HAR) based on channel state information (CSI) plays an increasingly important role in the research of human–computer interaction. Many CSI HAR models based on traditional machine learning methods and deep learning methods have encountered two challenges. A lot of CSI activity data is needed to train the HAR models, which is time consuming. When the indoor environment or scene changes, the recognition accuracy of the model drops significantly, so it is necessary to recollect data to train the model. The existing few-shot learning-based method can solve the above problems to some extent, but when there are more kinds of new activities or fewer shots, the recognition accuracy will decrease significantly. In this article, considering the relationship between various activity data, a graph-based few-shot learning method with dual attention mechanism (CSI-GDAM) is proposed to perform CSI-based HAR. The model uses a feature extraction layer, including the convolutional block attention module (CBAM), to extract activity-related information in CSI data. The difference and inner product of the feature vector of the CSI activity samples are used to realize the graph convolutional network with a graph attention mechanism. The experiments proved that under the learning task of recognizing new activities in the new environment, the recognition accuracy rates reached 99.74% and 98.42% in the 5-way 5-shot and 5-way 1-shot cases, respectively. The proposed method is also compared with other few-shot learning and transfer learning methods.

Collection and Utilization of Indoor Environmental Quality Information Using Affordable Image Sensing Technology

Incorporating Internet of Things (IoT) technology into the operation of buildings is expected to generate immense synergy, thereby saving energy and improving occupant comfort by overcoming the limitations of the existing system. Preventing operations in the absence of occupants can save energy, and the occupants’ preferred operating temperature should be used as the control set-point rather than the nominal temperature. In this study, IoT technology and image sensors are used to rapidly detect indoor environment changes, and a method is proposed to utilize air quality and thermal comfort as the control set-points. A real-time ventilation control algorithm is proposed based on the CO2 concentration calculated according to the number of occupants. To check the thermal comfort level, the real-time operating temperature estimated from the surface temperature data of the infrared array sensor is reflected in the comfort zone defined by the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE). The deficiencies in indoor environment conditions caused by the temporal and spatial lag of sensors in the old system are minimized using IoT technology, which also facilitates wireless communications. The image sensors can be used for multiple purposes based on various interpretations of the image information obtained.

A radio map self-updating algorithm based on mobile crowd sensing

The high cost of maintaining radio map is a major hurdle for wide application of WLAN fingerprint-based indoor localization. The development of mobile crowd sensing provides new possibilities, however the features of normal users such as moving freely and non -professional bring new challenges. In this paper, a radio map self-updating algorithm is proposed to resolve three key problems: the localization accuracy, determination of fingerprints need to be updated, and capture of new fingerprints. First we design the localization matrix mechanism and periodic adaptive estimate algorithm to ensure the localization accuracy. Second we propose the fingerprint integrity assessment algorithm to detect the access points changed and the periodic adaptive estimate algorithm to decide the update period for each reference point. Finally we design the active fingerprint collecting mode to update the radio map efficiently. The algorithm proposed has been deployed for real-world testing over 30 days, our studies show that it detects the network changes in indoor environment correctly in 98% cases, and automatically judges the localization accuracy in 95% cases. Meanwhile, the localization accuracy is stable and improved by over 40% even after long terms of deployment, and the overhead of user terminals is reduced over 40%.

Semantic and Context Based Image Retrieval Method Using a Single Image Sensor for Visual Indoor Positioning

Visual indoor positioning method plays an important role in indoor positioning since it does not require pre-deployed facilities. There are two problems in the traditional methods. On the one hand, the image retrieval is very time-consuming due to large number of images in the offline database. On the other hand, the positioning accuracy is also not ideal since there are many mismatching pairs between query image and matching image. In this paper, a novel visual indoor positioning method based on semantics is proposed to address the problem of low efficiency and poor accuracy. Firstly, we select indoor common and representative infrastructural objects as semantics to avoid decline of retrieval accuracy caused by the change of indoor environment. Meanwhile, we employ semantic extraction and classification for the collected images with common and representative infrastructural objects to establish the semantic based offline database. In addition, the semantic and context based image retrieval method is proposed to obtain the optimal matching image. Finally, the semantic constraint based feature point selection method is adopted to estimate the user’s position. The simulation results show that our proposed method can improve the efficiency and accuracy of visual positioning.

INDOOR POSITIONING FOR SMART DEVICES BASED ON SENSOR FUSION WITH PARTICLE FILTER: LOCALIZATION AND MAP UPDATING

Abstract. With every new generation of smart devices, new sensors are introduced, such as depth camera or UWB sensors. Combined with the rapidly growing number of smart mobile devices, indoor positioning systems (IPS) have seen increasing interest due to numerous indoor location-based services (ILBS) and mobile applications at large. Wi-Fi Received Signal Strength (RSS) based fingerprinting positioning (WF) techniques are popularly used in many IPS as the widespread deployment of IEEE 802.11 WLAN (Wi-Fi) networks, as this technique requires no line-of-sight to the access points (APs), and it is easy to extract Wi-Fi signal from 802.11 networks with smart devices. However, WF techniques have problems with fingerprint variance, i.e., fluctuation of the sensed signal, and efficient map updating due to the frequently changing environment. To address these problems, we propose a novel framework of IPS which uses particle filter to fuse WF and state-of-the-art CNN-based visual localization method to better adapt to changing indoor environment. The suggested system was tested with real-world crowdsourced data collected by multiple devices in an office hallway. The experimental results demonstrate that the system can achieve robust localization at a 0.3~1.5 m mean error (ME) accuracy, and map updating with a 79% correction rate.

A Novel Position and Orientation System for Pedestrian Indoor Mobile Mapping System

A reliable and accurate position and orientation system (POS) is essential for indoor mobile mapping system (MMS). We propose a novel pedestrian POS solution for indoor MMS using backpack INS, foot mounted pedestrian dead reckoning (Foot-PDR) and sparse control points with known coordinates. The Foot-PDR can provide continuous high precision positioning with the corrections of the control points to maintain its absolute accuracy. Such precise positioning is integrated with the backpack INS to estimate the accurate position and orientation, similar to the GNSS/INS integration for the outdoor environment. The lever-arm variation issue between the backpack INS and the Foot-PDR is solved by catching a specific phase of the gait cycle that has consistent relative pose between the back and the heel of pedestrians. The feasibility of the proposed POS was verified by an outdoor large-scale open-sky area and an indoor office environment tests. The outdoor experimental results demonstrate that the estimated horizontal position and yaw angle has an accuracy of 0.07 m and 0.6°, respectively (when the control points are as sparse as 50 meters). The indoor experimental results demonstrate that the position accuracy is 0.07 m, 0.08 m, and 0.04 m, along the north, east, and height directions. Furthermore, the proposed POS does not require expensive infrastructure support and is immune to indoor environment changes.

Indoor static localization based on Fresnel zones model using COTS Wi-Fi

Compared with outdoor localization using Global Positioning System (GPS), indoor localization is more complex and difficult due to the shadowing, fading and reflecting of radio. Thus, pioneers struggled for accurate device-free indoor localization and many creative schemes were proposed. However, these schemes still have defects and deficiencies. Mode-based schemes usually extract features of radio signal as fingerprints to match the target positions and signal modes. The training of features is time-consuming and labor-intensive because the fingerprints need to update every time when indoor environment changes. Model-based schemes universally establish empirical model to locate target, but the models are mostly un-robust in multiple experimental scenes. In this paper, we attempt to localize target in indoor environment based on Fresnel zones model of radio communication that could determine which elliptic region the target located in according to the phase of Channel State Information (CSI) obtained from Commercial Off-The-Shelf (COTS) Wi-Fi device. In addition, we focus on the interference on radio signal in indoor environment and propose specific solutions to remove phase offset and measurement error caused by insufficient hardware or background fluctuation. Therefore, our proposed system is more accurate compared with existing works. Results show the average error of our scheme is 33.6 cm in 47.6 m2 meeting room and 31.2 cm in 40.7 m2 office room while state-of-the-art work (MFDL) is 75 cm in 72 m2.

Discrete Hopfield neural network based indoor Wi-Fi localization using CSI

The fingerprint indoor localization method based on channel state information (CSI) has gained widespread attention. However, this method fails to provide a better localization effect and higher localization accuracy due to poor fingerprint accuracy, unsatisfactory classification and matching effect, and vulnerability to environmental impacts. In order to solve the problem, this paper proposes a CSI fingerprint indoor localization method based on the Discrete Hopfield Neural Network (DHNN). The method mainly consists of off-line and on-line phases. At the off-line phase, a low-pass filter is applied to conduct a preliminary processing on the fingerprint information of each reference point, and then, phase difference is adopted to correct the fingerprint data of all reference points. In this way, the quality of fingerprint data is improved, hence avoiding problems such as indoor environmental changes and multipath effect of signals, etc. in which impact the fingerprint data. Finally, the characteristic fingerprint database is established after acquiring relatively accurate fingerprint data. At the on-line phase, to maintain the consistency of data, the data of each reference point in the fingerprint database is set as an attractor. Meanwhile, the localization information of the test point is processed to make convergence judgment through DHNN. Eventually, the localization result is obtained. The experimental results show that the localization accuracy with a median error of 1.6 m can be achieved through the proposed method in the experimental environment. Compared with similar methods, it has a higher stability which can significantly reduce the cost of manpower and time.

A field study of the individual and combined effect of ventilation rate and lighting conditions on pupils’ performance

A field lab study was conducted to determine how much the indoor climate parameters, ventilation and lighting, influence children's academic abilities. The study involved 92 children, aged 10–12 years, who over four weeks answered a questionnaire and three different performance tests, which measured their processing speed, concentration, logical reasoning and math solving abilities. An experimental design was constructed to test the effect of changing the lighting from constant warm light with a maintained correlated colour temperature average of 2900 K and lighting level of 450 lux to a dynamic cool light of 4900 K and 750 lux. The change was tested at both low (3.9 l/s per person) and high ventilation rate (10.6 l/s per person). The results showed that processing speed (6.6%, P < 0.001), concentration (8.3%, P < 0.001) and math skills (11.8%, P < 0.006), improved the most in the combined scenario with high ventilation rate and dynamic cool lighting. The logical reasoning test did not show any significant changes. The questionnaire results of the pupils' perception of the indoor environment suggested that they were satisfied in general, but the indoor environmental changes had only very limited effect on how they perceived the classroom. The combination of dynamic lighting and increased ventilation rate indicated a boosted positive impact on the speed and concentration of the children which means that future renovations would benefit from a holistic design including both of these factors.

Performance based thermal comfort control (PTCC) using deep reinforcement learning for space cooling

With the recent increase in energy consumption in buildings, energy-saving strategies in buildings have become a priority in the energy policies of many countries. Therefore, many recent research studies have emphasized the advanced control methods to attain comfortable thermal conditions while minimizing the energy consumption in buildings. A new approach of thermal comfort control for space cooling/heating system is needed to reflect the changing indoor environment information in real time, and to control various factors (e.g., humidity, air velocity, etc.) that affect not only the temperature but also the thermal comfort.In this study, we propose the Gaussian process regression (GPR) for real-time thermal comfort prediction, a data-driven approach. These data-driven approaches will enable the monitoring of occupants and thermal comfort conditions based on real-time data and situational awareness. Then, based on the thermal comfort performance (PMV) prediction results obtained using the GPR, we investigated control methods involving the integration of systems, i.e., a variable refrigerant flow (VRF) system and a humidifier, instead of using simple set-temperature control for space cooling. For this purpose, deep Q-learning, which is an reinforcement learning method, was employed to derive the VRF and humidification integrated control methods. During zone operation, this algorithm learned an effective control policy based on rewards (thermal comfort and energy consumption) without relying on a thermal dynamics model. Moreover, by comparing the thermal comfort and energy consumption results with those obtained using fixed set-point (rule-based) control and performance-based comfort control for cooling, the efficiency of the proposed performance-based thermal comfort control (PTCC) was evaluated.As a results, it was found that PTCC yielded the optimal control action value that minimized the energy consumption while satisfying the thermal comfort conditions. In addition, applying the proposed PTCC strategy to cooling control could maintain the required performance level of thermal comfort by reflecting changing environmental conditions in real time, unlike the fixed set-point control.

An Adaptive Fingerprint Database Updating Method for Room Localization

Fingerprint-based indoor localization has become one of the most attractive and promising techniques, and however, one primary concern for this technology to be fully practical is to maintain the fingerprint database to combat harsh indoor environmental dynamics, especially for the large-scale and long-term deployment. In this paper, focusing on room localization, we first analyze the effect of different factors on indoor location accuracy, then propose an integrity check algorithm and a fuzzy map mechanism in response to network changes accurately and timely, and then design an accuracy check algorithm and a regional adaptive periodic update approach to update the fingerprint database effectively when an environment changes. Meanwhile, we design the active static data collecting mechanism and the active pin data collecting mechanism to ensure the accuracy and reliability of fingerprints newly captured and reduce the overhead on mobile devices significantly. The experimental results demonstrate that the proposed solution improves the performance of updating the fingerprint database in real time and robustness and maintains the location accuracy over 95% no matter how the indoor environmental changes; meanwhile, it also reduces energy efficiency imposed on the mobile phones by over 50%.

Two-step calibration for UWB-based indoor positioning system and positioning filter considering channel common bias

The channel error for ranging-based positioning generally considers the scale-factor error and bias. In this paper, the installation location error of anchor nodes (ANs) is additionally considered. To estimate these errors independently, a two-step calibration method is proposed. However, bias may vary depending on the changing indoor environment due to the non-line-of-sight error. Therefore, it is necessary to estimate bias in real-time at the positioning step. In this paper, a channel common bias (CCB) is defined, and a filter that can estimate not only the location of the mobile node but also the CCB is designed based on cubature Kalman filter (CKF). Experimental results show that the proposed two-step calibration method can accurately estimate scale-factor error and bias per channel, and location errors of all ANs. Also, it shows that the proposed CKF-based positioning filter considering CCB has faster channel error estimation performance than the filter considering channel-specific bias.

GEOMETRICAL NETWORK MODEL GENERATION USING POINT CLOUD DATA FOR INDOOR NAVIGATION

Abstract. Three-dimensional indoor navigation requires various functions, such as the shortest path retrieval, obstacle avoidance, and secure path retrieval, for optimal path finding using a geometrical network model. Although the geometrical network model can be prepared manually, the model should be automatically generated using images and point clouds to represent changing indoor environments. Thus, we propose a methodology for generating a geometrical network model for indoor navigation using point clouds through object classification, navigable area estimation, and navigable path estimation. Our proposed methodology was evaluated through experiments using the benchmark of the International Society for Photogrammetry and Remote Sensing for indoor modeling. In our experiments, we confirmed that our methodology can generate a geometrical network model automatically.

Real-Time Fire Monitoring and Visualization for the Post-Ignition Fire State in a Building

During a fire event, environmental threats to building occupants and first responders include extreme temperatures, toxic gases, disorientation due to poor visibility coupled with unfamiliar surroundings, and a changing indoor environment. In addition to these hazards, firefighters often lack critical information for making decisions once on the scene. The lack of information coupled with the dynamics of natural fire events leads to several near-misses, injuries, and deaths each year. Additionally, these challenges slow the rescue time of building occupants and prolong the suppression of the fire. Integrating real-time measurements from sensors into the fire intervention strategy may provide an opportunity for a new technological advancement to improve the practice of firefighting. In this study, a computational framework using Lightweight Communications and Marshalling was developed for connecting real-time fire data to an event detection sub-model to demonstrate how computing can be used for fire monitoring and sensor-assisted firefighting. A post-processed example using these monitoring computations in conjunction with a building information model is provided as a demonstration for presenting real-time data in the field. This work serves as a step towards an intelligent firefighting system based on real-time computing tools.

Prediction of Human Performance Using Electroencephalography under Different Indoor Room Temperatures

Varying indoor environmental conditions is known to affect office worker’s performance; wherein past research studies have reported the effects of unfavorable indoor temperature and air quality causing sick building syndrome (SBS) among office workers. Thus, investigating factors that can predict performance in changing indoor environments have become a highly important research topic bearing significant impact in our society. While past research studies have attempted to determine predictors for performance, they do not provide satisfactory prediction ability. Therefore, in this preliminary study, we attempt to predict performance during office-work tasks triggered by different indoor room temperatures (22.2 °C and 30 °C) from human brain signals recorded using electroencephalography (EEG). Seven participants were recruited, from whom EEG, skin temperature, heart rate and thermal survey questionnaires were collected. Regression analyses were carried out to investigate the effectiveness of using EEG power spectral densities (PSD) as predictors of performance. Our results indicate EEG PSDs as predictors provide the highest R2 (> 0.70), that is 17 times higher than using other physiological signals as predictors and is more robust. Finally, the paper provides insight on the selected predictors based on brain activity patterns for low- and high-performance levels under different indoor-temperatures.

Occupancy profiles modelling based on Indoor Measurements and Clustering Analysis: Application in an Office Building

Sensors were applied in an office building to obtain information regarding user presence and absence intervals. Occupancy was also recorded by manual observation, and indoor parameters such as air temperature, relative humidity, carbon dioxide (CO2), volatile organic compounds (VOC) were monitored. Occupants’ behaviors regarding door/window (open/closed) and electric power were considered. Clustering analysis by manual observation was employed to identify similarities in daily or monthly occupancy and to describe possible occupancy profiles. Similar approach was carried out with each monitored parameter and the results of clustering elaboration were compared with the real occupancy profiles to identify which sensor is more effective to measure office occupancy. Furthermore, data were analyzed to explore relationships between occupancy and the magnitude of indoor environmental changes with the objective to identify daily, weekly, or monthly patterns. Single-linkage, complete-linkage, and average-linkage clustering were applied to each dataset. The cophenetic correlation coefficient was used to verify the quality of the results obtained for each variable, and the complete linkage was selected to define the groups. Comparison between occupancy real data clustering and VOC and open/closed door groups demonstrated not similarities. The electricity consumption and CO2 data showed some similarities.Keywords: Occupancy detection, environmental sensor, clustering analysis, Office buildings