A Visual Indoor Localization Method Based on Efficient Image Retrieval

The task of indoor visual localization, utilizing camera visual information for user pose calculation, was a core component of Augmented Reality (AR) and Simultaneous Localization and Mapping (SLAM). Existing indoor localization technologies generally used scene-specific 3D representations or were trained on specific datasets, making it challenging to balance accuracy and cost when applied to new scenes. Addressing this issue, this paper proposed a universal indoor visual localization method based on efficient image retrieval. Initially, a Multi-Layer Perceptron (MLP) was employed to aggregate features from intermediate layers of a convolutional neural network, obtaining a global representation of the image. This approach ensured accurate and rapid retrieval of reference images. Subsequently, a new mechanism using Random Sample Consensus (RANSAC) was designed to resolve relative pose ambiguity caused by the essential matrix decomposition based on the five-point method. Finally, the absolute pose of the queried user image was computed, thereby achieving indoor user pose estimation. The proposed indoor localization method was characterized by its simplicity, flexibility, and excellent cross-scene generalization. Experimental results demonstrated a positioning error of 0.09 m and 2.14° on the 7Scenes dataset, and 0.15 m and 6.37° on the 12Scenes dataset. These results convincingly illustrated the outstanding performance of the proposed indoor localization method.

WiFi Indoor Positioning and Tracking Algorithm Based on Compressive Sensing and Sage-Husa Adaptive Kalman Filter

Aiming at the problem that the positioning accuracy of WiFi indoor positioning technology based on location fingerprint has not reached the requirements of practical application, a WiFi indoor positioning and tracking algorithm combining adaptive affine propagation (AAPC), compressed sensing (CS) and Kalman filter is proposed. In the off-line phase, AAPC algorithm is used to generate clustering fingerprints with optimal clustering effect performance;In the online phase, CS and nearest neighbor algorithm are used for position estimation;Finally, the Kalman filter and physical constraints are combined to perform positioning and tracking. By collecting a large number of real experimental data, it is proved that the developed algorithm has higher positioning accuracy and more accurate trajectory tracking effect.

Strong Tracking Particle Filter Based on the Chi-Square Test for Indoor Positioning

In recent years,a number of wireless indoor positioning(WIP),such as Bluetooth,Wi-Fi,and Ultra-Wideband(UWB)technologies,are emerging.However,the indoor environment is complex and changeable.Walls,pillars,and even pedestrians may block wireless signals and produce non-line-of-sight(NLOS)deviations,resulting in decreased positioning accuracy and the inability to provide people with real-time continuous indoor positioning.This work proposed a strong tracking particle filter based on the chi-square test(SPFC)for indoor positioning.SPFC can fuse indoor wireless signals and the information of the inertial sensing unit(IMU)in the smartphone and detect the NLOS deviation through the chi-square test to avoid the influence of the NLOS deviation on the final positioning result.Simulation experiment results show that the proposed SPFC can reduce the positioning error by 15.1%and 12.3% compared with existing fusion positioning systems in the LOS and NLOS environment.

Resilient tightly coupled INS/UWB integration method for indoor UAV navigation under challenging scenarios

Based on the high positioning accuracy,low cost and low-power consumption,the ultra-wide-band(UWB)is an ideal solution for indoor unmanned aerial vehicle(UAV)localization and navigation.However,the UWB signals are easy to be blocked or reflected by obstacles such as walls and furniture.A resilient tightly-coupled inertial navigation system(INS)/UWB integration is proposed and implemented for indoor UAV navigation in this paper.A factor graph optimization(FGO)method enhanced by resilient stochastic model is established to cope with the indoor challenging scenarios.To deal with the impact of UWB non-line-of-sight(NLOS)signals and noise uncertainty,the conventional neural net-works(CNNs)are introduced into the stochastic modelling to improve the resilience and reliability of the integration.Based on the status that the UWB features are limited,a‘two-phase'CNNs structure was designed and implemented:one for signal classification and the other one for measurement noise prediction.The proposed resilient FGO method is tested on flighting UAV platform under actual indoor challenging scenario.Compared to classical FGO method,the overall positioning errors can be decreased from about 0.60 m to centimeter-level under signal block and reflection scenarios.The superiority of resilient FGO which effectively verified in constrained environment is pretty important for positioning accuracy and integrity for indoor navigation task.

A Review of Device-Free Indoor Positioning for Home-Based Care of the Aged:Techniques and Technologies

With the development of urbanization,the problem of neurological diseases brought about by population aging has gradually become a social problem of worldwide concern.Aging leads to gradual degeneration of the central nervous system,shrinkage of brain tissue,and decline in physical function in many elderlies,making them susceptible to neurological diseases such as Alzheimer’s disease(AD),stroke,Parkinson’s and major depressive disorder(MDD).Due to the influence of these neurological diseases,the elderly have troubles such as memory loss,inability to move,falling,and getting lost,which seriously affect their quality of life.Tracking and positioning of elderly with neurological diseases and keeping track of their location in real-time are necessary and crucial in order to detect and treat dangerous and unexpected situations in time.Considering that the elderly with neurological diseases forget to wear a positioning device or have mobility problems due to carrying a positioning device,device-free positioning as a passive positioning technology that detects device-free individuals is more suitable than traditional active positioning for the home-based care of the elderly with neurological diseases.This paper provides an extensive and in-depth survey of device-free indoor positioning technology for home-based care and an in-depth analysis of the main features of current positioning systems,as well as the techniques,technologies andmethods they employ,fromthe perspective of the needs of the elderly with neurological conditions.Moreover,evaluation criteria and possible solutions of positioning techniques for the home-based care of the elderly with neurological conditions are proposed.Finally,the opportunities and challenges for the development of indoor positioning technology in 6G mobile networks for home-based care of the elderly with neurological diseases are discussed.This review has provided comprehensive and effective tracking and positioning techniques,technologies and methods for the elderly,by which we can obtain the location information of the elderly in real-time and make home-based care more comfortable and safer for the elderly with neurological diseases.

Robust Fingerprint Construction Based on Multiple Path Loss Model (M-PLM) for Indoor Localization

A robust radio map is essential in implementing a fingerprint-based indoor positioning system(IPS).However,the offline site survey to manually construct the radio map is time-consuming and labour-intensive.Various interpolation techniques have been proposed to infer the virtual fingerprints to reduce the time and effort required for offline site surveys.This paper presents a novel fingerprint interpolator using a multi-path loss model(MPLM)to create the virtual fingerprints from the collected sample data based on different signal paths from different access points(APs).Based on the historical signal data,the poor signal paths are identified using their standard deviations.The proposed method reduces the positioning errors by smoothing out the wireless signal fluctuations and stabilizing the signals for those poor signal paths.By consideringmultipath signal propagations from different APs,the inherent noise from these signal paths can be alleviated.Firstly,locations of the signal data with standard deviations higher than the threshold are identified.The new fingerprints are then generated at these locations based on the proposed M-PLM interpolation function to replace the old fingerprints.The proposed technique interpolates virtual fingerprints based on good signal paths with more stable signals to improve the positioning performance.Experimental results show that the proposed scheme enhances the positioning accuracy by up to 44%compared to the conventional interpolation techniques such as the Inverse DistanceWeighting,Kriging,and single Path LossModel.As a result,we can overcome the site survey problems for IPS by building an accurate radio map with more reliable signals to improve indoor positioning performance.

Research Status and Trends of Indoor Positioning and Navigation Technology in China

As an essential component of future comprehensive Positioning,Navigation,and Timing(PNT)system,indoor positioning technology has extensive application demands,making it a focal point of attention in both academia and industry.This article comprehensively reviews the research status of indoor positioning technology in China,with a focus on highlighting representative achievements and application validations from major research institutions in recent years.It addresses the challenges and issues faced in promotion and application of large-scale,high-precision indoor positioning.Furthermore,a universal and seamless indoor-outdoor positioning system architecture is proposed,along with a technical roadmap and key technologies to achieve this architecture.Finally,an analysis and outlook on future technological trends are presented.

Free-walking:Pedestrian inertial navigation based on dual foot-mounted IMU

The inertial navigation system(INS),which is frequently used in emergency rescue operations and other situations,has the benefits of not relying on infrastructure,high positioning frequency,and strong real-time performance.However,the intricate and unpredictable pedestrian motion patterns lead the INS localization error to significantly diverge with time.This paper aims to enhance the accuracy of zero-velocity interval(ZVI)detection and reduce the heading and altitude drift of foot-mounted INS via deep learning and equation constraint of dual feet.Aiming at the observational noise problem of low-cost inertial sensors,we utilize a denoising autoencoder to automatically eliminate the inherent noise.Aiming at the problem that inaccurate detection of the ZVI detection results in obvious displacement error,we propose a sample-level ZVI detection algorithm based on the U-Net neural network,which effectively solves the problem of mislabeling caused by sliding windows.Aiming at the problem that Zero-Velocity Update(ZUPT)cannot suppress heading and altitude error,we propose a bipedal INS method based on the equation constraint and ellipsoid constraint,which uses foot-to-foot distance as a new observation to correct heading and altitude error.We conduct extensive and well-designed experiments to evaluate the performance of the proposed method.The experimental results indicate that the position error of our proposed method did not exceed 0.83% of the total traveled distance.

Research and Development of Indoor Positioning

Indoor positioning systems have been sufficiently researched to provide location information of persons and devices.This paper is focused on the current research and further development of indoor positioning.The standard evolution and industry development are summarized.There are various positioning systems according to the scenarios,cost and accuracy.However,there is a basic positioning system framework including information extraction,measurement and calculation.In particular,the detailed positioning technologies mainly including cellular positioning and Local Area Network(LAN) positioning are listed and compared to provide a reference for practical applications.Finally,we summarize the challenges of indoor positioning and give a3-phase evolution route.

A Novel Indoor Positioning Framework

Current positioning systems are primarily based on the Global Positioning System(GPS).Although the GPS is accurate within 10 m,it is mainly used for outdoor positioning services(Location-Based Service;LBS).However,since satellite signals cannot penetrate buildings,indoor positioning has always been a blind spot for satellite signals.As indoor positioning applications are extensive with high commercial values,they have created a competitive niche in themarket.Existing indoor positioning technologies are unable to achieve less than 10 cmaccuracy except for the UltraWide Band(UWB)technology.On the other hand,the Bluetooth protocol achieves an accuracy of 1 to 2m.In this work,we use Bluetooth wireless signals to build a novel indoor positioning framework to avoid the high manufacturing costs involved in the UWB technology.The Bluetooth signals are combined with the results from artificial intelligence algorithms to improve accuracy.During laboratory indoor location tracking,the accuracy rate is 96%,which provides effective indoor tracking for the movement of people.

Position Vectors Based Efcient Indoor Positioning System

With the advent and advancements in the wireless technologies,Wi-Fi ngerprinting-based Indoor Positioning System(IPS)has become one of the most promising solutions for localization in indoor environments.Unlike the outdoor environment,the lack of line-of-sight propagation in an indoor environment keeps the interest of the researchers to develop efcient and precise positioning systems that can later be incorporated in numerous applications involving Internet of Things(IoTs)and green computing.In this paper,we have proposed a technique that combines the capabilities of multiple algorithms to overcome the complexities experienced indoors.Initially,in the database development phase,Motley Kennan propagation model is used with Hough transformation to classify,detect,and assign different attenuation factors related to the types of walls.Furthermore,important parameters for system accuracy,such as,placement and geometry of Access Points(APs)in the coverage area are also considered.New algorithm for deployment of an additional AP to an already existing infrastructure is proposed by using Genetic Algorithm(GA)coupled with Enhanced Dilution of Precision(EDOP).Moreover,classication algorithm based on k-Nearest Neighbors(k-NN)is used to nd the position of a stationary or mobile user inside the given coverage area.For k-NN to provide low localization error and reduced space dimensionality,three APs are required to be selected optimally.In this paper,we have suggested an idea to select APs based on Position Vectors(PV)as an input to the localization algorithm.Deducing from our comprehensive investigations,it is revealed that the accuracy of indoor positioning system using the proposed technique unblemished the existing solutions with signicant improvements.

Novel indoor positioning system based on ultra-wide bandwidth

To tackle challenges such as interference and poor accuracy of indoor positioning systems,a novel scheme based on ultra-wide bandwidth(UWB)technology is proposed.First,we illustrate a distance measuring method between two UWB devices.Then,a Taylor series expansion algorithm is developed to detect coordinates of the mobile node using the location of anchor nodes and the distance between them.Simulation results show that the observation error under our strategy is within 15 cm,which is superior to existing algorithms.The final experimental data in the hardware system mainly composed of STM32 and DW1000 also confirms the performance of the proposed scheme.

Optimization of MAC algorithm based on IEEE 802.15.4 in indoor positioning system

The mobility of the targets asks for high requirements of the locating speed in indoor positioning systems.The standard medium access control(MAC)algorithm will often cause lots of packet conflicts and high transmission delay if multiple users communicate with one beacon at the same time,which will severely limit the speed of the system.Therefore,an optimized MAC algorithm is proposed based on channel reservation to enable users to reserve beacons.A frame threshold is set to ensure the users with shorter data frames do not depend on the reservation mechanism,and multiple users can achieve packets switching with relative beacon in a fixed sequence by using frequency division multiplexing technology.The simulation results show that the optimized MAC algorithm proposed in this paper can improve the positioning speed significantly while maintaining the positioning accuracy.Moreover,the positioning accuracy can be increased to a certain extent if more channel resources can be obtained,so as to provide effective technical support for the location and tracking applications of indoor moving targets.

Research on Indoor Passive Positioning Technology Based on WiFi

In recent years,WiFi indoor positioning technology has become a hot research topic at home and abroad.However,at present,indoor positioning technology still has many problems in terms of practicability and stability,which seriously affects the accuracy of indoor positioning and increases the complexity of the calculation process.Aiming at the instability of RSS and the more complicated data processing,this paper proposes a low-frequency filtering method based on fast data convergence.Low-frequency filtering uses MATLAB for data fitting to filter out low-frequency data;data convergence combines the mean and multi-data parallel analysis process to achieve a good balance between data volume and system performance.At the same time,this paper combines the position fingerprint and the relative position method in the algorithm,which reduces the error on the algorithm system.The test results show that the strategy can meet the requirements of indoor passive positioning and avoid a large amount of data collection and processing,and the average positioning error is below 0.5 meters.

Single-Site Indoor Fingerprint Localization Based on MIMO-CSI

In order to achieve higher accuracy and lower cost of indoor localization,we propose a positioning method using multiple input and multiple output(MIMO)channel state information(CSI)as a fingerprint.The method can be divided into three stages,feature extraction,offline training and online localization.In the feature extraction,the segmented average and principal component analysis(PCA)are used to reduce the data dimension and decrease system complexity.In the offline training,the deep neural network(NN)model is trained to implement the position classification.In the online localization,the data are input into the trained NN model first,and then its output is further processed by weighted k-nearest neighbor(WKNN)technology to estimate the position.Experimental results show that the proposed method can significantly reduce the positioning error compared to other methods and the average error is 1.39m in a complex indoor environment.

Barom etric altim eter in wireless com m unication network indoor positioning system

A differential barometric altimetry technology based on the digital pressure sensors is put forward by using the existing mobile phone base station as reference. The height of known base sta- tion is precise. The pressure and temperature of the known base station is measured by sensors and transmitted to users. The absolute height value of user will be calculated by combining the baromet- ric pressure values and temperature values from the base station with the locally measured values. In order to decrease system errors caused by inconsistency between the measured pressure value at base station and the locally measured pressure value, weights correction is applied based on multiple reference stations. The calculated height value is accurate due to eliminating the measured errors caused by irregular changes of atmospheric pressure, with the error less than 1 m. Resolution of ele- vation positioning depends upon the resolution of the pressure sensor, the relationship between which is approximately linear. When the resolution of sensor is 0.01 hPa, the resolution of elevation positioning is about 0. 1 m. In addition, the data frame format at base station is designed in this arti- cle. Experimental results show that the method is accurate, reliable, stable and has the ability to distinguish floors and stair steps.

DFF-EDR:An Indoor Fingerprint Location Technology Using Dynamic Fusion Features of Channel State Information and Improved Edit Distance on Real Sequence

Positioning technology based on wireless network signals in indoor environments has developed rapidly in recent years as the demand for locationbased services continues to increase.Channel state information(CSI)can be used as location feature information in fingerprint-based positioning systems because it can reflect the characteristics of the signal on multiple subcarriers.However,the random noise contained in the raw CSI information increases the likelihood of confusion when matching fingerprint data.In this paper,the Dynamic Fusion Feature(DFF)is proposed as a new fingerprint formation method to remove the noise and improve the feature resolution of the system,which combines the pre-processed amplitude and phase data.Then,the improved edit distance on real sequence(IEDR)is used as a similarity metric for fingerprint matching.Based on the above studies,we propose a new indoor fingerprint positioning method,named DFF-EDR,for improving positioning performance.During the experimental stage,data were collected and analyzed in two typical indoor environments.The results show that the proposed localization method in this paper effectively improves the feature resolution of the system in terms of both fingerprint features and similarity measures,has good anti-noise capability,and effectively reduces the localization errors.

Multi-cluster-center based filtering algorithm and its application to WLAN indoor positioning

Wireless local area network(WLAN) is developing to a ubiquitous technique in daily life.As a related product,WLAN based indoor positioning system is attracting more and more concern.Fingerprint is a mainstream method of wireless indoor positioning.However,it still has some shortcomings of that received signal strength(RSS) is multi-modal and sensitive to environmental factors.These characters would have a negative effect on the performance of positioning system.In this paper,a filtering algorithm based on multi-cluster-center is proposed.We make full use of this algorithm to optimize the training samples at off-line phase to improve the performance of non-linear fitting with the fingerprint feature,and further enhance the positioning accuracy.Finally,we use multiple sets of original WLAN signal samples and signal samples after filtering as the training input of positioning system respectively.After that,the results analysis is demonstrated.Simulation results show that it is a reliable algorithm to enhance the performance of WLAN indoor positioning.

Enhanced Fingerprinting Based Indoor Positioning Using Machine Learning

Due to the inability of the Global Positioning System(GPS)signals to penetrate through surfaces like roofs,walls,and other objects in indoor environments,numerous alternative methods for user positioning have been presented.Amongst those,the Wi-Fi fingerprinting method has gained considerable interest in Indoor Positioning Systems(IPS)as the need for lineof-sight measurements is minimal,and it achieves better efficiency in even complex indoor environments.Offline and online are the two phases of the fingerprinting method.Many researchers have highlighted the problems in the offline phase as it deals with huge datasets and validation of Fingerprints without pre-processing of data becomes a concern.Machine learning is used for the model training in the offline phase while the locations are estimated in the online phase.Many researchers have considered the concerns in the offline phase as it deals with huge datasets and validation of Fingerprints becomes an issue.Machine learning algorithms are a natural solution for winnowing through large datasets and determining the significant fragments of information for localization,creating precise models to predict an indoor location.Large training sets are a key for obtaining better results in machine learning problems.Therefore,an existing WLAN fingerprinting-based multistory building location database has been used with 21049 samples including 19938 training and 1111 testing samples.The proposed model consists of mean and median filtering as pre-processing techniques applied to the database for enhancing the accuracy by mitigating the impact of environmental dispersion and investigated machine learning algorithms(kNN,WkNN,FSkNN,and SVM)for estimating the location.The proposed SVM with median filtering algorithm gives a reduced mean positioning error of 0.7959 m and an improved efficiency of 92.84%as compared to all variants of the proposed method for 108703 m^(2) area.

MagneFi: Multiuser, Multi-Building and Multi-Floor Geomagnetic Field Dataset for Indoor Positioning

Indoor positioning and localization have emerged as a potential research area during the last few years owing to the wide proliferation of smartphones and the inception of location-attached services for the consumer industry.Due to the importance of precise location information,several positioning technologies are adopted such as Wi-Fi,ultrawideband,infrared,radio frequency identification,Bluetooth beacons,pedestrian dead reckoning,and magnetic field,etc.Although Wi-Fi and magnetic field-based positioning are more attractive concerning the deployment of Wi-Fi access points and ubiquity of magnetic field data,the latter is preferred as it does not require any additional infrastructure as other approaches do.Despite the advantages of magnetic field positioning,comparing the performance of positioning and localization algorithms is very difficult due to the lack of good public datasets that cover various aspects of the magnetic field data.Available datasets do not provide the data to analyze the impact of device heterogeneity,user heights,and time-specific magnetic field mutation.Moreover,multi-floor and multibuilding data are available for the evaluation of state-of-the-art approaches.To overcome the above-mentioned issues,this study presents multi-user,multidevice,multi-building magnetic field data which is collected over a longer period.The dataset contains the data from five different smartphones including Samsung Galaxy S8,S9,A8,LG G6,and LG G7 for three geographically separated buildings.Three users including one female and two males collected the data for various path geometry and data collection scenarios.Moreover,the data contains the magnetic field samples collected on stairs to test multifloor localization.Besides the magnetic field data,the data from inertial measurement unit sensors like the accelerometer,motion sensors,and barometer is provided as well.