Indoor Localization Method Research Articles

A survey of indoor positioning systems based on a six-layer model

Indoor positioning has attracted considerable interest in both the industry and academic communities because of its wide range of applications, such as asset tracking, healthcare and context-aware services like targeted advertisements. While there are many indoor localisation methods, each has its advantages and disadvantages, taking into consideration various factors such as the effect of the indoor environment, ease of implementation, computational cost, positioning accuracy, etc. In other words, no single solution can cater for all different situations. Although many survey papers have been published on indoor positioning, new techniques and methods are proposed every year, so it is important to stay abreast of its latest developments. In addition, each survey has its own classification for indoor positioning systems without a common scheme. Inspired by the well-known OSI model and TCP/IP model, it would be desirable to develop a systematic framework for studying indoor positioning systems. In this paper, we make this new contribution by introducing a systemic survey framework based on a six-layer model to give a comprehensive survey of indoor positioning systems, namely: device layer, communication layer, network layer, data layer, method layer and application layer. Complementing the previous survey papers, this paper provides a survey of the latest research works on indoor positioning based on the six-layer model. Our emphasis is on systematic categorisation, machine learning-based enhancements, collaborative localisation and COVID-19-related applications. The six-layer model should provide a useful framework and new insights for the research community.

Deep Learning-Based Geomagnetic Navigation Method Integrated with Dead Reckoning

Accurate location information has significant commercial and economic value as they are widely used in intelligent manufacturing, material localization and smart homes. Magnetic sequence-based approaches show great promise mainly due to their pervasiveness and stability. However, existing geomagnetic indoor localization methods are facing the problems of location ambiguity and feature extraction deficiency, which will lead to large localization errors. To address these issues, we propose a coarse-to-fine geomagnetic indoor localization method based on deep learning. First, a multidimensional geomagnetic feature extraction method is presented which can extract magnetic features from spatial and temporal aspects. Then, a hierarchical deep neural network model is devised to extract more accurate geomagnetic information and corresponding location clues for more accurate localization. Finally, localization is achieved through a particle filter combined with IMU localization. To evaluate the performance of the proposed methods, we carried out several experiments at three trial paths with two heterogeneous devices, Vivo X30 and Huawei Mate30. Experimental results demonstrate that the proposed algorithm can achieve more accurate localization performance than the state-of-the-art methods. Meanwhile, the proposed algorithm has low cost and good pervasiveness for different devices.

Localization of Mobile Robots Based on Depth Camera

In scenarios of indoor localization of mobile robots, Global Positioning System (GPS) signals are prone to loss due to interference from urban building environments and cannot meet the needs of robot localization. On the other hand, traditional indoor localization methods based on wireless signals such as Bluetooth and WiFi often require the deployment of multiple devices in advance, and these methods can only obtain distance information and are unable to obtain the attitude of the positioning target in space. This paper proposes a method for the indoor localization of mobile robots based on a depth camera. Firstly, we extracted ORB feature points from images captured by a depth camera and performed homogenization processing. Then, we performed feature matching between adjacent two frames of images, and the mismatched points are eliminated to improve the accuracy of feature matching. Finally, we used the Iterative Closest Point (ICP) algorithm to estimate the camera’s pose, thus achieving the localization of mobile robots in indoor environments. In addition, an experimental evaluation was conducted on the TUM dataset of the Technical University of Munich to validate the feasibility of the proposed depth-camera-based indoor localization system for mobile robots. The experimental results show that the average localization accuracy of our algorithm on three datasets is 0.027 m, which can meet the needs of indoor localization for mobile robots.

Neural-Network-Based Localization Method for Wi-Fi Fingerprint Indoor Localization.

Despite the high demand for Internet location service applications, Wi-Fi indoor localization often suffers from time- and labor-intensive data collection processes. This study proposes a novel indoor localization model that utilizes fingerprinting technology based on a convolutional neural network to address this issue. The aim is to enhance Wi-Fi indoor localization by streamlining the data collection process. The proposed indoor localization model leverages a 3D ray-tracing technique to simulate the wireless received signal strength intensity (RSSI) across the field. By incorporating this advanced technique, the model aims to improve the accuracy and efficiency of Wi-Fi indoor localization. In addition, an RSSI heatmap fingerprint dataset generated from the ray-tracing simulation is trained on the proposed indoor localization model. To optimize and evaluate the model's performance in real-world scenarios, experiments were conducted using simulated datasets obtained from the publicly available databases of UJIIndoorLoc and Wireless InSite. The results show that the new approach solves the problem of resource limitation while achieving a verification accuracy of up to 99.09%.

Indoor location recognition device and method based on indoor robot

This paper describes an indoor location recognition device and method based on an indoor robot. The device includes a computer Raspberry Pi, a lidar, a CMOS camera, an ultrasonic sensor, a first microcontroller, and a servo motor; the lidar is connected to the computer Raspberry Pi. Pie, the lidar is driven by a servo motor, the ultrasonic sensor is connected to the computer Raspberry Pi through the first microcontroller, and the computer Raspberry Pi controls the lidar to scan the location to measure the width and length of the current location, and controls the ultrasonic sensor to the location. Perform up and down scanning to measure the distance between the ultrasonic sensor and the ground and the ceiling respectively, and obtain the height of the room; the CMOS camera is connected to the computer Raspberry Pi, and the computer Raspberry Pi sends an instruction to control the CMOS camera to take pictures of objects and places and use them Opencv and other related technologies achieve the purpose of identification. The device and method can continuously identify indoor locations under various complex conditions, and at the same time improve the accuracy and reliability of location recognition.

Pedestrian Flow Identification and Occupancy Prediction for Indoor Areas.

Indoor localization is used to locate objects and people within buildings where outdoor tracking tools and technologies cannot provide precise results. This paper aims to improve analytics research, focusing on data collected through indoor localization methods. Smart devices recurrently broadcast automatic connectivity requests. These packets are known as Wi-Fi probe requests and can encapsulate various types of spatiotemporal information from the device carrier. In addition, in this paper, we perform a comparison between the Prophet model and our implementation of the autoregressive moving average (ARMA) model. The Prophet model is an additive model that requires no manual effort and can easily detect and handle outliers or missing data. In contrast, the ARMA model may require more effort and deep statistical analysis but allows the user to tune it and reach a more personalized result. Second, we attempted to understand human behaviour. We used historical data from a live store in Dubai to forecast the use of two different models, which we conclude by comparing. Subsequently, we mapped each probe request to the section of our place of interest where it was captured. Finally, we performed pedestrian flow analysis by identifying the most common paths followed inside our place of interest.

A Robust Indoor Localization Method Based on DAT-SLAM and Template Matching Visual Odometry

Robust positioning is a central issue in robots. Due to the complex indoor environment, visual simultaneous localization and mapping (VSLAM) is susceptible to light and some scenes contain few features. The robot cannot obtain an accurate position. To solve these problems, this work first proposes dynamic adaptive threshold simultaneous localization and mapping (DAT-SLAM), a method for extracting feature points based on a dynamic adaptive threshold. The method is stable for feature extraction under illumination transformation. Subsequently, template matching visual odometry (VO) is introduced and combined with DAT-SLAM to form a joint indoor localization framework. Experiments on the datasets show that DAT-SLAM has a better performance than oriented fast and rotated brief SLAM2 (ORB-SLAM2). The mean positioning accuracy is improved by 11.74%. Experiments in real scenes show that the joint localization framework can achieve continuous localization in scenes with sparse features.

GConvLoc: WiFi Fingerprinting-Based Indoor Localization Using Graph Convolutional Networks

We propose GConvLoc, a WiFi fingerprinting-based indoor localization method utilizing graph convolutional networks. Using the graph structure, we can consider the fingerprint data of the reference points and their location labels in addition to the fingerprint data of the test point at inference time. Experimental results show that GConvLoc outperforms baseline methods that do not utilize graphs.

An Indoor Location Method Based on Features Optimization for Different Regions With Improved Curve Smoothness Index

An Indoor Location Method Based on Features Optimization for Different Regions With Improved Curve Smoothness Index

LUVI: Lightweight UWB-VIO based relative positioning for AR-IoT applications

In this paper, we propose LUVI, Lightweight UWB-VIO relative positioning method for indoor localization. Recent designs of handheld and embedded devices feature various technologies which have the means to enhance localization performance in indoor environments. These include visual odometry based on cameras and augmented reality, and communication hardware such as UWB. Integration of such technologies to exploit their advantages allows us to compensate for each other's errors in measurement. This improves the overall function of future services, such as visual representation of sensing information from sensors in areas that are not physically visible. However, existing work cannot fully exploit these technologies to high extent, often inducing high errors or wasted resources. LUVI is a novel localization method which estimates the location of a target object using relative coordinates of estimator devices without the aid of definitive coordinates. LUVI focuses on utilization of lightweight management of virtual anchors for localization, with functions that reduce the computing and communication complexity while maintaining the accuracy and improving energy efficiency of the localization. Our work has been fully implemented and tested in several indoor environments, showing robustness to NLOS while significantly reducing computational complexity, and up to 30% lower average error.

Direction of Arrival Method for L-Shaped Array with RF Switch: An Embedded Implementation Perspective

This paper addresses the challenge of implementing Direction of Arrival (DOA) methods for indoor localization using Internet of Things (IoT) devices, particularly with the recent direction-finding capability of Bluetooth. DOA methods are complex numerical methods that require significant computational resources and can quickly deplete the batteries of small embedded systems typically found in IoT networks. To address this challenge, the paper presents a novel Unitary R-D Root MUSIC for L-shaped arrays that is tailor-made for such devices utilizing a switching protocol defined by Bluetooth. The solution exploits the radio communication system design to speed up execution, and its root-finding method circumvents complex arithmetic despite being used for complex polynomials. The paper carries out experiments on energy consumption, memory footprint, accuracy, and execution time in a commercial constrained embedded IoT device series without operating systems and software layers to prove the viability of the implemented solution. The results demonstrate that the solution achieves good accuracy and attains an execution time of a few milliseconds, making it a viable solution for DOA implementation in IoT devices.

Scene Recognition for Device-Free Indoor Localization

Device-free indoor localization methods based on Channel State Information (CSI) have become an increasingly important topic. In complex indoor environments, both the line-of-sight (LOS) and non-LOS areas coexist, and the optimal parameters of the localization models for these two areas are different. In order to address this problem, in this paper, the scene-recognition indoor localization method is proposed to identify LOS and NLOS areas. First, the scene recognition model is given by combining mutation particle swarm optimization (MPSO) with backpropagation (BP) neural network. Then, the feature transformation method based on discriminant correlation analysis (DCA) is presented, which can effectively explore the correlation of amplitude data and phase data and form high-resolution location fingerprints, and further improve the indoor localization accuracy. Experimental results show that compared with the PAIL, LCAF, LSTM, BLS, PCNB, ABPS and FapFi algorithms, the proposed algorithm has higher localization accuracy.

Graph-Based Radio Fingerprint Augmentation for Deep-Learning-Based Indoor Localization

Indoor localization plays an essential role in enabling location-based services (LBSs) for sensor networks wherever global navigation satellite systems (GNSSs) are unreachable. For deep-learning-based indoor localization methods, radio data are extremely important for accurate fingerprinting-based localization but are often incomplete or are not temporally and spatially sufficient for data acquisition. Motivated by the goal of leveraging existing radio fingerprints to further improve localization accuracy, in this article, we propose a graph-based fingerprint augmentation method for deep-learning-based indoor localization. In this method, by modeling each reference point (RP) as a vertex of a graph and its radio data as graph signals, we develop a graph signal model where virtual RPs are introduced as missing vertices with missing radio data. Then, we explore the underlying spatial structure among all the real and virtual RPs to find the graph Laplacian with which to reconstruct the radio fingerprints by a semisupervised graph interpolation. On this basis, some deep neural networks and convolutional neural networks (DNNs and CNNs) trained by the reconstructed radio fingerprints are developed for indoor localization. Experiments on real datasets reveal the performance gain of the proposed fingerprint augmentation method in localization accuracy, showing the potential of graph-based data augmentation for deep-learning-based indoor localization.

User data selection method using received signal strength indicator for semi-supervised learning in fingerprint localization

The fingerprint technique is used as an indoor localization method. This study uses a CNN-based indoor fingerprint localization method. The estimation accuracy of CNN improves as the number of AP information (AP identifiers and received signal strength indicator) increases. However, gathering AP information is time-consuming and costly. The problem can be solved using UD (AP information users measured). However, the UD measuring method does not know the user’s exact location. Therefore, it is essential to choose UD that is accurately estimate and use it for CNN training. In this study, we propose a method for selecting UDs that makes use of the RSSI similarity between AP information and UD.

Indoor Localization Method for a Mobile Robot Using LiDAR and a Dual AprilTag

Global localization is one of the important issues for mobile robots to achieve indoor navigation. Nowadays, most mobile robots rely on light detection and ranging (LiDAR) and adaptive Monte Carlo localization (AMCL) to realize their localization and navigation. However, the reliability and performance of global localization only using LiDAR are restricted due to the monotonous sensing feature. This study proposes a global localization approach to improve mobile robot global localization using LiDAR and a dual AprilTag. Firstly, the spatial coordinate system constructed with two neighboring AprilTags is applied as the reference basis for global localization. Then, the robot pose can be estimated by generating precise initial particle distribution for AMCL based on the relative tag positions. Finally, in pose tracking, the count and distribution of AMCL particles, evaluating the certainty of localization, is continuously monitored to update the real-time position of the robot. The contributions of this study are listed as follows. (1) Compared to the localization method only using LiDAR, the proposed method can locate the robot’s position with a few iterations and less computer power consumption. (2) The failure localization issues due to the many similar indoor features can be solved. (3) The error of the global localization can be limited to an acceptable range compared to the result using a single tag.

Indoor 2D Positioning Method for Mobile Robots Based on the Fusion of RSSI and Magnetometer Fingerprints.

Received signal strength indicator (RSSI)-based fingerprinting is a widely used technique for indoor localization, but these methods suffer from high error rates due to various reflections, interferences, and noises. The use of disturbances in the magnetic field in indoor localization methods has gained increasing attention in recent years, since this technology provides stable measurements with low random fluctuations. In this paper, a novel fingerprinting-based indoor 2D positioning method, which utilizes the fusion of RSSI and magnetometer measurements, is proposed for mobile robots. The method applies multilayer perceptron (MLP) feedforward neural networks to determine the 2D position, based on both the magnetometer data and the RSSI values measured between the mobile unit and anchor nodes. The magnetic field strength is measured on the mobile node, and it provides information about the disturbance levels in the given position. The proposed method is validated using data collected in two realistic indoor scenarios with multiple static objects. The magnetic field measurements are examined in three different combinations, i.e., the measurements of the three sensor axes are tested together, the magnetic field magnitude is used alone, and the Z-axis-based measurements are used together with the magnitude in the X-Y plane. The obtained results show that significant improvement can be achieved by fusing the two data types in scenarios where the magnetic field has high variance. The achieved results show that the improvement can be above 35% compared to results obtained by utilizing only RSSI or magnetic sensor data.

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.

A Novel Deep Learning Approach to 5G CSI/Geomagnetism/VIO Fused Indoor Localization.

For positioning tasks of mobile robots in indoor environments, the emerging positioning technique based on visual inertial odometry (VIO) is heavily influenced by light and suffers from cumulative errors, which cannot meet the requirements of long-term navigation and positioning. In contrast, positioning techniques that rely on indoor signal sources such as 5G and geomagnetism can provide drift-free global positioning results, but their overall positioning accuracy is low. In order to obtain higher precision and more reliable positioning, this paper proposes a fused 5G/geomagnetism/VIO indoor localization method. Firstly, the error back propagation neural network (BPNN) model is used to fuse 5G and geomagnetic signals to obtain more reliable global positioning results; secondly, the conversion relationship from VIO local positioning results to the global coordinate system is established through the least squares principle; and finally, a fused 5G/geomagnetism/VIO localization system based on the error state extended Kalman filter (ES-EKF) is constructed. The experimental results show that the 5G/geomagnetism fusion localization method overcomes the problem of low accuracy of single sensor localization and can provide more accurate global localization results. Additionally, after fusing the local and global positioning results, the average positioning error of the mobile robot in the two scenarios is 0.61 m and 0.72 m. Compared with the VINS-mono algorithm, our approach improves the average positioning accuracy in indoor environments by 69.0% and 67.2%, respectively.

FlexTDOA: Robust and Scalable Time-Difference of Arrival Localization Using Ultra-Wideband Devices

In this paper, we propose FlexTDOA, an indoor localization method and a system using ultra-wideband (UWB) radios. Our method uses time-difference of arrival (TDOA) localization so that the user device remains passive and is able to compute its location simply by listening to the communication between the fixed anchors, ensuring the scalability of our system. The anchors communicate using a custom-designed, flexible time-division multiple-access (TDMA) scheme in which time is divided in slots, and in each slot one anchor interrogates one or more anchors which respond in the same slot. Clock synchronization between the anchors is not needed. We implemented FlexTDOA on in-house designed hardware using a commercial UWB module. We compare FlexTDOA against the classic TDOA approach and range-based localization in a deployment of ten anchors and one tag, both with and without obstructions. Results show that FlexTDOA achieves the highest localization accuracy in most of the scenarios. We also evaluate the localization accuracy under multiple conditions by varying parameters such as the number of responses, their order, and the number of anchors. We simulate and evaluate the effect of the physical speed of the tag on the choice of optimum system parameters.

An Efficient Indoor Localization Based on Deep Attention Learning Model

Indoor localization methods can help many sectors, such as healthcare centers, smart homes, museums, warehouses, and retail malls, improve their service areas. As a result, it is crucial to look for low-cost methods that can provide exact localization in indoor locations. In this context, image-based localization methods can play an important role in estimating both the position and the orientation of cameras regarding an object. Image-based localization faces many issues, such as image scale and rotation variance. Also, image-based localization’s accuracy and speed (latency) are two critical factors. This paper proposes an efficient 6-DoF deep-learning model for image-based localization. This model incorporates the channel attention module and the Scale Pyramid Module (SPM). It not only enhances accuracy but also ensures the model’s real-time performance. In complex scenes, a channel attention module is employed to distinguish between the textures of the foregrounds and backgrounds. Our model adapted an SPM, a feature pyramid module for dealing with image scale and rotation variance issues. Furthermore, the proposed model employs two regressions (two fully connected layers), one for position and the other for orientation, which increases outcome accuracy. Experiments on standard indoor and outdoor datasets show that the proposed model has a significantly lower Mean Squared Error (MSE) for both position and orientation. On the indoor 7-Scenes dataset, the MSE for the position is reduced to 0.19 m and 6.25° for the orientation. Furthermore, on the outdoor Cambridge landmarks dataset, the MSE for the position is reduced to 0.63 m and 2.03° for the orientation. According to the findings, the proposed approach is superior and more successful than the baseline methods.