FM Based Localization: A Proposed Improvement

Most UAVs rely on GPS for localization in an outdoor environment. However, in GPS-denied environment, other sources of localization are required for UAVs to conduct feedback control and navigation. LiDAR has been used for indoor localization, but the sampling rate is usually too low for feedback control of UAVs. To compensate this drawback, IMU sensors are usually fused to generate high-frequency odometry, with only few extra computation resources. To achieve this goal, a real-time LiDAR inertial odometer system (RTLIO) is developed in this work to generate high-precision and high-frequency odometry for the feedback control of UAVs in an indoor environment, and this is achieved by solving cost functions that consist of the LiDAR and IMU residuals. Compared to the traditional LIO approach, the initialization process of the developed RTLIO can be achieved, even when the device is stationary. To further reduce the accumulated pose errors, loop closure and pose-graph optimization are also developed in RTLIO. To demonstrate the efficacy of the developed RTLIO, experiments with long-range trajectory are conducted, and the results indicate that the RTLIO can outperform LIO with a smaller drift. Experiments with odometry benchmark dataset (i.e., KITTI) are also conducted to compare the performance with other methods, and the results show that the RTLIO can outperform ALOAM and LOAM in terms of exhibiting a smaller time delay and greater position accuracy.

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Investigations and analysis of indoor environment quality of green and conventional shopping mall buildings based on customers' perception

Building and Environment ◽

10.1016/j.buildenv.2020.106851 ◽

2020 ◽

Vol 177 ◽

pp. 106851 ◽

Cited By ~ 4

Author(s):

Xiaohui Du ◽

Yongchao Zhang ◽

Zhengquan Lv

Keyword(s):

Indoor Environment ◽

Shopping Mall ◽

Environment Quality ◽

Indoor Environment Quality

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An Indoor Localization System Using Residual Learning with Channel State Information

Entropy ◽

10.3390/e23050574 ◽

2021 ◽

Vol 23 (5) ◽

pp. 574

Author(s):

Chendong Xu ◽

Weigang Wang ◽

Yunwei Zhang ◽

Jie Qin ◽

Shujuan Yu ◽

...

Keyword(s):

Channel State Information ◽

Indoor Environment ◽

Indoor Localization ◽

Location Based Services ◽

Channel State ◽

State Information ◽

Localization Accuracy ◽

Localization System ◽

Increasing Demand ◽

Degradation Problem

With the increasing demand of location-based services, neural network (NN)-based intelligent indoor localization has attracted great interest due to its high localization accuracy. However, deep NNs are usually affected by degradation and gradient vanishing. To fill this gap, we propose a novel indoor localization system, including denoising NN and residual network (ResNet), to predict the location of moving object by the channel state information (CSI). In the ResNet, to prevent overfitting, we replace all the residual blocks by the stochastic residual blocks. Specially, we explore the long-range stochastic shortcut connection (LRSSC) to solve the degradation problem and gradient vanishing. To obtain a large receptive field without losing information, we leverage the dilated convolution at the rear of the ResNet. Experimental results are presented to confirm that our system outperforms state-of-the-art methods in a representative indoor environment.

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Detection of Legionella species, the influence of precipitation on the amount of Legionella DNA, and bacterial microbiome in aerosols from outdoor sites near asphalt roads in Toyama Prefecture, Japan

BMC Microbiology ◽

10.1186/s12866-021-02275-2 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Jun-ichi Kanatani ◽

Masanori Watahiki ◽

Keiko Kimata ◽

Tomoko Kato ◽

Kaoru Uchida ◽

...

Keyword(s):

16S Rrna ◽

16S Rrna Gene ◽

Indoor Environment ◽

Geometric Mean ◽

Amplicon Sequencing ◽

Positive Sample ◽

Outdoor Environment ◽

Rrna Gene ◽

Total Precipitation ◽

Monthly Total Precipitation

Abstract Background Legionellosis is caused by the inhalation of aerosolized water contaminated with Legionella bacteria. In this study, we investigated the prevalence of Legionella species in aerosols collected from outdoor sites near asphalt roads, bathrooms in public bath facilities, and other indoor sites, such as buildings and private homes, using amoebic co-culture, quantitative PCR, and 16S rRNA gene amplicon sequencing. Results Legionella species were not detected by amoebic co-culture. However, Legionella DNA was detected in 114/151 (75.5%) air samples collected near roads (geometric mean ± standard deviation: 1.80 ± 0.52 log10 copies/m3), which was comparable to the numbers collected from bathrooms [15/21 (71.4%), 1.82 ± 0.50] but higher than those collected from other indoor sites [11/30 (36.7%), 0.88 ± 0.56] (P < 0.05). The amount of Legionella DNA was correlated with the monthly total precipitation (r = 0.56, P < 0.01). It was also directly and inversely correlated with the daily total precipitation for seven days (r = 0.21, P = 0.01) and one day (r = − 0.29, P < 0.01) before the sampling day, respectively. 16S rRNA gene amplicon sequencing revealed that Legionella species were detected in 9/30 samples collected near roads (mean proportion of reads, 0.11%). At the species level, L. pneumophila was detected in 2/30 samples collected near roads (the proportion of reads, 0.09 and 0.11% of the total reads number in each positive sample). The three most abundant bacterial genera in the samples collected near roads were Sphingomonas, Streptococcus, and Methylobacterium (mean proportion of reads; 21.1%, 14.6%, and 1.6%, respectively). In addition, the bacterial diversity in outdoor environment was comparable to that in indoor environment which contains aerosol-generating features and higher than that in indoor environment without the features. Conclusions DNA from Legionella species was widely present in aerosols collected from outdoor sites near asphalt roads, especially during the rainy season. Our findings suggest that there may be a risk of exposure to Legionella species not only in bathrooms but also in the areas surrounding asphalt roads. Therefore, the possibility of contracting legionellosis in daily life should be considered.

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Deep learning approach for UHF RFID-based indoor localization

International Journal of RF Technologies ◽

10.3233/rft-200256 ◽

2021 ◽

pp. 1-13

Author(s):

Haishu Ma ◽

Zongzheng Ma ◽

Lixia Li ◽

Ya Gao

Keyword(s):

Radio Frequency Identification ◽

Indoor Environment ◽

Indoor Localization ◽

Estimation Error ◽

Research Direction ◽

Linear Interpolation ◽

Linear Mapping ◽

Uhf Rfid ◽

Indoor Location ◽

Main Research

Due to the proliferation of the IoT devices, indoor location-based service is bringing huge business values and potentials. The positioning accuracy is restricted by the variability and complexity of the indoor environment. Radio Frequency Identification (RFID), as a key technology of the Internet of Things, has became the main research direction in the field of indoor positioning because of its non-contact, non-line-of-sight and strong anti-interference abilities. This paper proposes the deep leaning approach for RFID based indoor localization. Since the measured Received Signal Strength Indicator (RSSI) can be influenced by many indoor environment factors, Kalman filter is applied to erase the fluctuation. Furthermore, linear interpolation is adopted to increase the density of the reference tags. In order to improve the processing ability of the fingerprint database, deep neural network is adopted together with the fingerprinting method to optimize the non-linear mapping between fingerprints and indoor coordinates. The experimental results show that the proposed method achieves high accuracy with a mean estimation error of 0.347 m.

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Developing an integrated framework for optimization of spatial requirements of construction equipment

Built Environment Project and Asset Management ◽

10.1108/bepam-07-2020-0126 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

S. Hemalattha ◽

R. Vidjeapriya

Keyword(s):

Construction Projects ◽

Indoor Environment ◽

Outdoor Environment ◽

Building Information Modelling ◽

Actual Structure ◽

Content Type ◽

Space Planning ◽

Existing Structures ◽

Building Information

PurposeThis study aims to develop a framework for optimizing the spatial requirements of the equipment in a construction site using a geographic information system (GIS).Design/methodology/approachAn ongoing construction project, an existing thermal powerplant in India, is considered to be the case study, and the corresponding construction activities were scheduled. The equipment spaces were defined for the scheduled activities in building information modelling (BIM), which was further imported to GIS to define the topology rules, validate and optimize the spatial requirements. The BIM simulates the indoor environment, which includes the actual structure being constructed, and the GIS helps in modelling the outdoor environment, which includes the existing structures, temporary facilitates, topography of the site, etc.; thus, this study incorporates the knowledge of BIM in a geospatial environment to obtain optimized equipment spaces for various activities.FindingsSpace in construction projects is to be considered as a resource as well as a constraint, which is to be modelled and planned according to the requirements. The integration of BIM and GIS for equipment space planning will enable precise identification of the errors in the equipment spaces defined and also result in fewer errors as possible. The integration has also eased the process of assigning the topology rules and validating the same, which otherwise is a tedious process.Originality/valueThe workspace for each activity will include the space of the equipment. But, in most of the previous works of workspace planning, only the labour space is considered, and the conflicts and congestions occurring due to the equipment were neglected. The planning of equipment spaces cannot be done based only on the indoor environment; it has to be carried out by considering the surroundings and topography of the site, which have not been researched extensively despite its importance.

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Exploration of an Electroactive Polymer as an Actuator for Microrobotics

MRS Proceedings ◽

10.1557/proc-785-d8.5 ◽

2003 ◽

Vol 785 ◽

Cited By ~ 1

Author(s):

C. Bielmeier ◽

W. Walter

Keyword(s):

Steady State ◽

Power Consumption ◽

Parallel Plate ◽

Electroactive Polymers ◽

Electroactive Polymer ◽

Low Power Consumption ◽

Parallel Plate Capacitor ◽

Steady State Current ◽

A Current ◽

Over Time

ABSTRACTThe development of lightweight low power consumption actuators is critical to the development of micro-robotics. Electroactive Polymers (EAP), i.e. Nafion N-117, meet these requirements. In the actuation of an EAP, the current does not remain constant over time. The development of a circuit model of current draw over time to best predict a current dynamic has been explored. While the material mimics a parallel plate capacitor, it has been found that capacitance plays no role in achieving steady state current levels. This development is critical to understanding and developing the material as an actuator.

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Static and Dynamic Evaluation of an UWB Localization System for Industrial Applications

Sci ◽

10.3390/sci2010007 ◽

2020 ◽

Vol 2 (1) ◽

pp. 7 ◽

Cited By ~ 2

Author(s):

Mickaël Delamare ◽

Remi Boutteau ◽

Xavier Savatier ◽

Nicolas Iriart

Keyword(s):

Motion Capture ◽

Indoor Environment ◽

Indoor Localization ◽

Ground Truth ◽

Industrial Applications ◽

Ultra Wideband ◽

Complex Environments ◽

Experimental Protocol ◽

Localization Systems ◽

Do So

Many applications in the context of Industry 4.0 require precise localization. However, indoor localization remains an open problem, especially in complex environments such as industrial environments. In recent years, we have seen the emergence of Ultra WideBand (UWB) localization systems. The aim of this article is to evaluate the performance of a UWB system to estimate the position of a person moving in an indoor environment. To do so, we implemented an experimental protocol to evaluate the accuracy of the UWB system both statically and dynamically. The UWB system is compared to a ground truth obtained by a motion capture system with a millimetric accuracy.

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Improving Accuracy of the Alpha–Beta Filter Algorithm Using an ANN-Based Learning Mechanism in Indoor Navigation System

Sensors ◽

10.3390/s19183946 ◽

2019 ◽

Vol 19 (18) ◽

pp. 3946 ◽

Cited By ~ 9

Author(s):

Faisal Jamil ◽

Do Hyeun Kim

Keyword(s):

Inertial Measurement Unit ◽

Indoor Environment ◽

Indoor Localization ◽

Indoor Navigation ◽

Prediction Algorithm ◽

Measurement Unit ◽

Sensor Reading ◽

Inertial Measurement ◽

Learning Module ◽

Alpha Beta

The navigation system has been around for the last several years. Recently, the emergence of miniaturized sensors has made it easy to navigate the object in an indoor environment. These sensors give away a great deal of information about the user (location, posture, communication patterns, etc.), which helps in capturing the user’s context. Such information can be utilized to create smarter apps from which the user can benefit. A challenging new area that is receiving a lot of attention is Indoor Localization, whereas interest in location-based services is also rising. While numerous inertial measurement unit-based indoor localization techniques have been proposed, these techniques have many shortcomings related to accuracy and consistency. In this article, we present a novel solution for improving the accuracy of indoor navigation using a learning to perdition model. The design system tracks the location of the object in an indoor environment where the global positioning system and other satellites will not work properly. Moreover, in order to improve the accuracy of indoor navigation, we proposed a learning to prediction model-based artificial neural network to improve the prediction accuracy of the prediction algorithm. For experimental analysis, we use the next generation inertial measurement unit (IMU) in order to acquired sensing data. The next generation IMU is a compact IMU and data acquisition platform that combines onboard triple-axis sensors like accelerometers, gyroscopes, and magnetometers. Furthermore, we consider a scenario where the prediction algorithm is used to predict the actual sensor reading from the noisy sensor reading. Additionally, we have developed an artificial neural network-based learning module to tune the parameter of alpha and beta in the alpha–beta filter algorithm to minimize the amount of error in the current sensor readings. In order to evaluate the accuracy of the system, we carried out a number of experiments through which we observed that the alpha–beta filter with a learning module performed better than the traditional alpha–beta filter algorithm in terms of RMSE.

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Static and Dynamic Evaluation of an UWB Localization System for Industrial Applications

Sci ◽

10.3390/sci1030062 ◽

2019 ◽

Vol 1 (3) ◽

pp. 62 ◽

Cited By ~ 2

Author(s):

Mickael Delamare ◽

Remi Boutteau ◽

Xavier Savatier ◽

Nicolas Iriart

Keyword(s):

Motion Capture ◽

Indoor Environment ◽

Indoor Localization ◽

Ground Truth ◽

Industrial Applications ◽

Ultra Wideband ◽

Complex Environments ◽

Experimental Protocol ◽

Localization Systems ◽

Do So

Many applications in the context of Industry 4.0 require precise localization. However, indoor localization remains an open problem, especially in complex environments such as industrial environments. In recent years, we have seen the emergence of Ultra WideBand (UWB) localization systems. The aim of this article is to evaluate the performance of a UWB system to estimate the position of a person moving in an indoor environment. To do so, we implemented an experimental protocol to evaluate the accuracy of the UWB system both statically and dynamically. The UWB system is compared to a ground truth obtained by a motion capture system with a millimetric accuracy.

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