Complex Indoor Environments Research Articles

Carrier Phase Ranging for Indoor Positioning With 5G NR Signals

Indoor positioning is one of the core technologies of Internet of Things (IoT) and artificial intelligence (AI), and is expected to play a significant role in the upcoming era of AI. However, affected by the complexity of indoor environments, it is still highly challenging to achieve continuous and reliable indoor positioning. Currently, 5G cellular networks are being deployed worldwide, the new technologies of which have brought the approaches for improving the performance of wireless indoor positioning. In this paper, we investigate the indoor positioning under the 5G new radio (NR), which has been standardized and being commercially operated in massive markets. Specifically, a solution is proposed and a software defined receiver (SDR) is developed for indoor positioning. With our SDR indoor positioning system, the 5G NR signals are firstly sampled by universal software radio peripheral (USRP), and then, coarse synchronization is achieved via detecting the start of the synchronization signal block (SSB). Then, with the assistance of the pilots transmitted on the physical broadcasting channel (PBCH), multipath acquisition and delay tracking are sequentially carried out to estimate the time of arrival (ToA) of received signals. Furthermore, to improve the ToA ranging accuracy, the carrier phase of the first arrived path is estimated. Finally, to quantify the accuracy of our ToA estimation method, indoor field tests are carried out in an office environment, where a 5G NR base station (known as gNB) is installed for commercial use. Our test results show that, in the static test scenarios, the ToA accuracy measured by the 1-{\sigma} error interval is about 0.5 m, while in the pedestrian mobile environment, the probability of range accuracy within 0.8 m is 95%.

Grid-optimized UAV indoor path planning algorithms in a complex environment

Path planning has become a predominant issue for unmanned aerial vehicles (UAVs), especially in complex indoor environments. Existing solutions for UAV indoor path planning are strongly limited due to their high computational complexity, slow convergence speed and poor flight paths. In this study, a set of grid-optimized UAV indoor path planning algorithms targeting complex environments is proposed to solve the abovementioned issues. First, the grid-optimized indoor airspace modeling (GO-IAM) algorithm based on the Geo-graphical coordinates Subdividing grid with One-dimension integral coding on 2n-Tree in 3 Dimensions (GeoSOT-3D) is proposed to reduce the computational complexity of modeling the indoor 3D airspace by means of spatial subdivisions. For regular indoor scenarios and a complex “dead zone” airspace, the grid-optimized A* path planning (GO-APP) algorithm and the grid-optimized local backtracking path planning (GO-LBPP) algorithm are established to address the efficiency and flyability of path planning. Comparison experiments of multiple UAV indoor path planning algorithms reveal that the GO-APP algorithm has the shortest computation time and planning path. Specifically, with the use of the GO-LBPP algorithm, the indoor local dead zone issue can be solved, and the deadlock of the planning path can be avoided, which cannot be achieved by other algorithms. Hence, the proposed grid-optimized UAV indoor path planning algorithms can realize state-of-the-art path planning through spatial subdivision in complex 3D indoor airspace.

Combining Multichannel RSSI and Vision with Artificial Neural Networks to Improve BLE Trilateration

The demands for accurate positioning and navigation applications in complex indoor environments such as emergency call positioning, fire-fighting services, and rescue operations are increasing continuously. Indoor positioning approaches apply different types of sensors to increase the accuracy of the user’s position. Among these technologies, Bluetooth Low Energy (BLE) appeared as a popular alternative due to its low cost and energy efficiency. However, BLE faces challenges related to Received Signal Strength Indicator (RSSI) fluctuations caused by human body shadowing. This work presents a method to compensate RSSI values by applying Artificial Neural Network (ANN) algorithms to RSSI measurements from three BLE advertising channels and a wearable camera as an additional source of information for the presence or absence of human obstacles. The resulting improved RSSI values are then converted into ranges using path loss models, and trilateration is applied to obtain indoor localization. The proposed artificial system provides significantly better localization solutions than fingerprinting or trilateration using uncorrected RSSI values.

WiFi-Based Indoor Positioning and Communication: Empirical Model and Theoretical Analysis

GPS (Global Positioning System) has been developed more mature and perfect in outdoor positioning. However, because GPS signals cannot be effectively transmitted in a complex indoor environment, positioning and navigation cannot be performed in typical situations such as indoor emergency occurrence location determination, shopping mall guides, and intelligent service robot fixed-point services. With the deepening of related researches, technologies based on ultrasound, WiFi, Bluetooth, radio frequency identification, infrared, etc. have been used to achieve indoor target positioning. The traditional WiFi-based indoor positioning technology uses the RSS value and CSI value of the WiFi signal to build a fingerprint database for positioning. The algorithm is complex and highly cost, and the function is relatively single. In this paper, for the first time, we propose a new algorithm for integrating indoor target positioning and communication based on WiFi signals. Using RSS value of WiFi signal for target location and using CSI value for indoor wireless channel modeling to get the system transmission function to achieve communication purpose, the experimental results show that the positioning algorithm can achieve positioning within the ideal accuracy range, and the communication algorithm part can accurately recover the system function of the wireless multipath channel and finally achieve the goal of integration of positioning and communication based on the WiFi signal level.

RF-Gait: Gait-Based Person Identification with COTS RFID

Recently, person identification has been a prerequisite in many applications of the Internet of Things. As a new biometric identification technology, gait recognition has a wide application prospect with the advantages of long-distance recognition and difficulty to forge. However, the existing gait recognition methods have some problems, such as complex algorithm calculation, high user participation, and large equipment overhead. In this paper, we propose RF-Gait, a method that identifies a person through unobtrusive gait perception with COTS RFID. The key insight is that wireless signal fluctuation can be exploited to distinguish each person’s unique gait behavior. To this end, we first collect and preprocess the gait-induced data composed of multiple RFID tags. Furthermore, multivariate variational mode decomposition is utilized to extract the intrinsic features in the spatial multichannels cooperatively. By developing a support vector machine model, we identify a person via the intrinsic walking pattern. Finally, extensive experiments show that our method can identify a person with an average accuracy of 96.3% from a group of twenty persons in a complex indoor environment.

IFC2INDOORGML: AN OPEN-SOURCE TOOL FOR GENERATING INDOORGML FROM IFC

Abstract. The interest in 3D indoor models has been continuously growing. Most such models are made available as point clouds or BIM (e.g., IFC), the former being generally provided as unstructured information while the latter comes highly structured and rich in semantic information. IFC models are consequently more suitable for direct use, but they can be very complex and contain too many details, which often raises privacy concerns. IndoorGML is one of the standards for describing 3D indoor space with the purpose of supporting Location Based Services (LBS). It relies on solid scientific concepts and offers a high flexibility with extension mechanisms. It provides a geometric, topological, and semantic description of the indoor which facilitates specifically applications like indoor navigation or facility management. Additionally, it can represent complex indoor environments without compromising privacy, thanks to its high level of abstraction. However, despite its solid conceptual basis, IndoorGML is suffering from a lack of practical tools and remains hard to produce, making it largely unavailable. In this project, we developed an open-source tool named ifc2indoorgml allowing to automatically generate IndoorGML models from IFC data. We discuss the workflow and the different development approaches. By making such tool available to the wider public, we expect more 3D IndoorGML models to be created and made freely available for research and development within the spatial community and beyond.

An Indoor and Outdoor Multi-Source Elastic Fusion Navigation and Positioning Algorithm Based on Particle Filters

In terms of indoor and outdoor positioning, in recent years, researchers at home and abroad have proposed some multisource integrated navigation and positioning methods, but these methods are navigation and positioning methods for a single scene. When it comes to the switching of indoor and outdoor complex scenes, these methods will cause the results of position with a marked jump and affect the accuracy of navigation and positioning. Aiming at the navigation and positioning problem in the case of indoor and outdoor complex scene switching, this paper proposes a multisource elastic navigation and positioning method based on particle filters, which makes full use of the redundant information of multisource sensors, constructs an elastic multisource fusion navigation and positioning model after eliminating abnormal data, elastically gives different particle weights to multisource sensors according to environmental changes and realizes the elastic fusion and positioning of multisource sensors through filtering. The test results show that this method has high navigation and positioning accuracy, the dynamic positioning accuracy is better than 0.7 m and there will be no jumping of positioning results in the process of scene switching, which improves the navigation and positioning accuracy and stability in complex and changeable indoor and outdoor environments.

DuLoc: Dual-Channel Convolutional Neural Network Based on Channel State Information for Indoor Localization

With the increasing demand for location-based services in indoor environments, research on indoor localization technology has become an urgent need. In complex indoor environments, strong multipath effects can greatly interfere with the propagation of wireless signals, which may seriously degrade localization accuracy. To solve the problem, this paper proposes an indoor localization system DuLoc based on fine-grained subcarriers and a dual-channel convolutional neural network (CNN). In this system, abnormal sampling points are removed by the Isolation Forest method to deal with environmental noise. In order to select more stable and effective signals, the number of clusters of each subcarrier is analyzed by the Mean Shift clustering method, and the fingerprint database is divided into two sub-databases according to the threshold. The dual-channel CNN model is constructed to optimize the classification results with the data characteristics. Those two types of subcarrier data construct the input feature images to train the dual-channel CNN model. Then the final location coordinate is calculated by the probability weighted centroid method. Experimental results are presented to demonstrate that DuLoc has higher localization accuracy in complex indoor environments compared with the other existing methods.

INDOOR VISIBLE LIGHT LOCALIZATION METHOD BASED ON EMBEDDED ARTIFICIAL INTELLIGENCE

Abstract. This paper proposes an indoor visible light location method based on embedded platform and optical frequency image recognition technology with artificial intelligence, which can effectively improve the location effect in complex indoor environment. By transplanting the artificial intelligence (AI) based image classification algorithm into the embedded platform, this method uses a forward neural network to analyse the position information coming from the coded optical frequency image received by a camera, and then the positioning results can be obtained. In view of the "motion state" and "occlusion state" that are most likely to fail in traditional visible image localization, we specially supplement the training set of relevant characteristic optical frequency images to enhance the robustness of the method. According to the track and positioning results of the moving platform receiver, the proposed method can provide accurate and reliable navigation and positioning and has stronger anti-interference ability compared with the traditional light intensity or light image positioning methods.

Research on Compression Sensing Positioning Algorithm of Indoor Complex Environment Visible Light Indoor Based on Hybrid APIT.

In today's highly urbanized world, indoor space is becoming more extensive and more complex, and under the increasingly urgent needs, indoor positioning has attracted people's attention. With the rapid development of LED lighting technology, indoor positioning technology based on visible light communication has many advantages over traditional indoor positioning technology. Aiming at the influence of environmental factors such as noise and reflected light on the positioning accuracy, the compression perception theory is applied to the localization of visible light. The position of the receiving end in the positioning space is defined as a sparse variable in the discrete space. The power measurement matrix is expressed as the product of the observation matrix, and the sparse matrix and sparse vector in the compression perception theory are expressed. The traditional APIT algorithm is easy to misjudge unknown nodes in the triangle, resulting in low positioning accuracy of the algorithm. In this study, an indoor visible positioning algorithm based on hybrid APIT is proposed, which uses the area relationship of the triangle to determine the initial position of the unknown node, and then uses the tangent circle to further narrow the area where the unknown node may be located, and uses the hybrid centroid localization algorithm to obtain the estimated position of the unknown node.

Wi-Exercise: An Indoor Human Movement Detection Method Based on Bidirectional LSTM Attention

Human motion recognition has become a hot topic in the field of human-computer interaction. Due to the traditional method of detecting human movement in indoor environments, there are still problems such as high hardware costs, influenced by the environment and the need for experimenters to be equipped with relevant equipment. With the large-scale deployment of wireless devices and the establishment of wireless network infrastructure, indoor personnel movement recognition systems based on wireless signals are gradually proposed. In this paper, we propose a device-independent motion-detection method Wi-exercise based on temporal recurrent neural networks. The acquired channel state information (CSI) is preprocessed by wavelet transform, and the output CSI matrix is filtered by the principal component analysis (PCA) to extract the feature values. The wavelet function is processed to further remove the noise also to make the action data more distinctive, and then PCA is used to track the entire CSI time series to improve and maintain its dimensional energy. The preprocessed data are subjected to feature extraction to obtain CSI of human motion and establish feature sequences, and then the action model is trained using the bidirectional long short-term memory attention (ABLSTM) mechanism to finally realize human motion recognition. Finally, the robustness of Wi-exercise is tested experimentally, and its performance is compared with that of existing recognition methods. The experimental results show that the average recognition rate of the method is 94.43% in the complex indoor environment, and the method is better than the traditional classifier method for action recognition, with lower and higher recognition accuracy.

Monocular vision-based time-to-collision estimation for small drones by domain adaptation of simulated images

Recently, there is an increasing demand for small drones owing to their small size and agility in complex indoor environments. Accordingly, safety issues for navigating small drones become of significant importance. For drones to be able to navigate safely through complex environments, it would be useful to estimate accurate time-to-collision (TTC) to obstacles. To this end, in this paper, we propose a deep learning-based TTC estimation algorithm. To train generalizable neural networks for TTC estimation, large datasets including collision cases are needed. However, in real-world environments, it is impractical and infeasible to collide drones with obstacles to collect a significant amount of data. Simulation environments could facilitate the data acquisition procedure, but the data from simulated environments could be quite different from those of real environments, which is commonly termed as reality-gap. In this study, to reduce this reality-gap, sim-to-real methods based on a variant of the generative adversarial network are used to convert simulated images into real world-like synthetic images. Besides, to consider the uncertainties that come from using the synthetic dataset, the aleatoric loss function and Monte Carlo dropout method are employed. Furthermore, we improve the performance of the deep learning-based TTC estimation algorithm by replacing conventional convolutional neural networks (CNNs) with convolutional long short-term memory (ConvLSTM) layers which are known to be better at handling time-series data than CNNs. To validate the performance of the proposed framework, real flight experiments have been carried out in various indoor environments. Our proposed framework decreases the average TTC estimation error by 0.21 s compared with the baseline approach with CNNs.

Device-Free Multitarget Localization With Weighted Intersection Multidimensional Feature for Passive UHF RFID

The device-free localization (DFL) can locate the target without requiring the target to carry any radio equipment, which has a broad application prospect in the field of indoor positioning. Radio frequency identification (RFID), as a key technology for IoT implementation, has been widely used in objects identification and tracking. RFID-based radio tomographic imaging (RTI) method has received extensive attention due to its low cost, excellent real-time performance and easy to deployment. However, the indoor multipath effect, which leads to unpredictable variations of the received signal strength (RSS), especially in multi-target positioning. It is still a challenging problem to recognize the locations of multiple targets effectively in the complex indoor environment. In this paper, a novel method of intersection multidimensional feature analysis is proposed to solve this problem. The target impact region is introduced to extract the effective intersections, and then the multidimensional feature information is analyzed to determine the region where the real target may exist. The location estimation of the target is obtained by assigning appropriate weights to each intersection in the region. Extensive experiments are conducted with results are demonstrating that the method achieves high localization accuracy and localization stability in different indoor scenarios, while maintaining low computational complexity.

Precise 3D Indoor Localization and Trajectory Optimization Based on Sparse Wi-Fi FTM Anchors and Built-In Sensors

Indoor location-based services have become more and more important due to their potential applications in a wide range of personalized services in recent years. The accuracy of smartphone based 3D indoor localization is subjected to the poor performance of low-cost sensors and limited coverage of location sources. In order to solve these problems, this paper proposes a precise 3D indoor localization and trajectory optimization framework that uses the combination of sparse Wi-Fi Fine Time Measurement (FTM) anchors and built-in sensors (3D-LOWS). The inertial navigation system (INS) mechanization, multi-level constraints and observed values are integrated by the adaptive unscented Kalman filter to eliminate effects of cumulative error, indoor magnetic interference, and diversity of handheld modes. The Wi-Fi based ranging and landmark detection information is used to provide an accurate absolute reference to the built-in sensors based method. In addition, this paper proposes and evaluates two different trajectory optimization algorithms and compares the improved localization performance. The comprehensive experiments indicate that the proposed 3D-LOWS is proved to achieve accurate and stable 3D indoor positioning and trajectory optimization performance under complex indoor environments using sparse wireless stations.

Improved-UWB/LiDAR-SLAM Tightly Coupled Positioning System with NLOS Identification Using a LiDAR Point Cloud in GNSS-Denied Environments

Reliable absolute positioning is indispensable in long-term positioning systems. Although simultaneous localization and mapping based on light detection and ranging (LiDAR-SLAM) is effective in global navigation satellite system (GNSS)-denied environments, it can provide only local positioning results, with error divergence over distance. Ultrawideband (UWB) technology is an effective alternative; however, non-line-of-sight (NLOS) propagation in complex indoor environments severely affects the precision of UWB positioning, and LiDAR-SLAM typically provides more robust results under such conditions. For robust and high-precision positioning, we propose an improved-UWB/LiDAR-SLAM tightly coupled (TC) integrated algorithm. This method is the first to combine a LiDAR point cloud map generated via LiDAR-SLAM with position information from UWB anchors to distinguish between line-of-sight (LOS) and NLOS measurements through obstacle detection and NLOS identification (NI) in real time. Additionally, to alleviate positioning error accumulation in long-term SLAM, an improved-UWB/LiDAR-SLAM TC positioning model is constructed using UWB LOS measurements and LiDAR-SLAM positioning information. Parameter solving using a robust extended Kalman filter (REKF) to suppress the effect of UWB gross errors improves the robustness and positioning performance of the integrated system. Experimental results show that the proposed NI method using the LiDAR point cloud can efficiently and accurately identify UWB NLOS errors to improve the performance of UWB ranging and positioning in real scenarios. The TC integrated method combining NI and REKF achieves better positioning effectiveness and robustness than other comparative methods and satisfactory control of sensor errors with a root-mean-square error of 0.094 m, realizing subdecimeter indoor positioning.

Towards monocular vision-based autonomous flight through deep reinforcement learning

This paper proposes an obstacle avoidance strategy for small multi-rotor drones with a monocular camera using deep reinforcement learning. The proposed method is composed of two steps: depth estimation and navigation decision making. For the depth estimation step, a pre-trained depth estimation algorithm based on the convolutional neural network is used. On the navigation decision making step, a dueling double deep Q-network is employed with a well-designed reward function. The network is trained using the robot operating system and Gazebo simulation environment. To validate the performance and robustness of the proposed approach, simulations and real experiments have been carried out using a Parrot Bebop2 drone in various complex indoor environments. We demonstrate that the proposed algorithm successfully travels along the narrow corridors with the texture free walls, people, and boxes.

Low-Trap-Density CsPbX3 Film for High-Efficiency Indoor Photovoltaics.

The continuous advancement of the Internet of Things (IoT) and photovoltaic technology has promoted the development of indoor photovoltaics (IPVs) that powers wireless devices. Nowadays, the CsPbX3 perovskite has received widespread attention because of its high power conversion efficiency (PCE) in an indoor environment and suitable band gap for IPVs. In this work, we regulated the thickness of the photoactive layer (to optimize the carrier transport process without affecting indoor absorption) and bromine substitution (to adjust the band gap and improve the quality of the film) to reduce trap-assisted carrier recombination. A CsPbI2.7Br0.3 perovskite cell with excellent performance was obtained, which is superior to c-Si cells in a low-light environment. The optimized device achieved PCE values of 32.69 and 33.11% under a 1000 lux fluorescent lamp and white light-emitting diode (WLED) illumination. The J-V hysteresis of the device is also effectively suppressed. Moreover, it has a steady-state output power of 7.76 μW (0.09 cm2, and can be enhanced by enlarging the areas), which can meet the consumption of many small wireless devices. It is worth noting that the optimized device has excellent applicability to be used in a complex indoor environment.

Stereo visual-inertial odometry using structural lines for localizing indoor wheeled robots

This paper proposes optimization-based stereo visual-inertial odometry (VIO) to locate indoor wheeled robots. The multiple Manhattan worlds (MWs) assumption is adopted to model the interior environment. Instead of treating these worlds as isolated ones, we fuse the latest MW with the previous ones if they are in the same direction, reducing the calculated errors on the orientation of the latest MW. Then, the structural lines that encode the orientation information of these worlds are taken as additional landmarks to improve the positioning accuracy and reduce the accumulated drift of the system, especially when the system is in a challenging environment (i.e. scenes with continuous turning and low textures). In addition, the structural lines are parameterized by only two variables, which improves the computational efficiency and simplifies the initialization of lines. Experiments on public benchmark datasets and in real-world environments demonstrate that the proposed VIO system can accurately position the wheeled robot in a complex indoor environment.

Early indoor occluded fire detection based on firelight reflection characteristics

Indoor fire detection is a challenging task and plays a key role in disaster management. The early stage of a fire is the best stage to extinguish the fire. However, early detection of indoor fires is difficult because the early stages of fires are easily occluded in complex indoor environments. Therefore, a method based on firelight reflection characteristics is proposed for early fire detection in occluded indoor environments. First, the characteristics of fires occluded by complex environments are described by analyzing the characteristics of firelight reflection. Second, a highly sensitive method for foreground recognition is developed through use of strategic background updates and a block binarization threshold, which are suitable for detecting the weak changes caused by occluded fires in videos. Finally, a multiexpert system is established for occluded fire detection by extracting the changing characteristics of the area in which the firelight reflection occurs, including spectral variability, motion persistence, and regional expansion. The accuracy and run time of the system are evaluated based on a large dataset to verify our method. Moreover, our proposed approach is discussed in detail in terms of effectiveness and applicability, and the results show that our method can be effectively applied in indoor occluded fire detection.

The Intuitive Supervision Model (ISM) using Convolution Neural Networks (CNN) and Unscented Kalman Filters (UKF)

Radio frequency identification technology is one of the fastest-growing technologies in the realms of navigation, medical, robotics, communication system, logistics, security, safety, etc. Surveillance is one of the important fields where high accuracy and fast response are needed. In this research work, RFID sensors are used to track moving objects with an intelligent supervision model. The sophisticated surveillance model employs neural networks followed by an adaptive filtering technique based on an Unscented Kalman filter. A neural network is also one of the most efficient and powerful technology in the field of learning and data processing capability. A neural network has the capability of processing a mammoth amount of data because of this feature its efficiency and accuracy are quite high. This model localizes N number of objects/targets through an intelligent surveillance model, picks a random object from this pool of localized objects to track, categorizes their movement through a controlled checkpoint, and calculates the distance traveled by the moving object /target. Experimental results show that the proposed model can locate multiple-objects with the help of multiple input RFID antennas and tags and track them concerning to the RFID antennas with high accuracy and stability in the complex indoor environment and this intuitive model can be effectively implemented at the airport, railway station, shopping mall, retail management, as well as any other surveillance purpose. For this research work number of authors work, is reviewed and based on literature review this model is designed.