Indoor Localization Method Research Articles

A Novel NLOS Acoustic Signal Identification Method for Indoor Localization Based on Machine Learning

A Novel NLOS Acoustic Signal Identification Method for Indoor Localization Based on Machine Learning

A Review of Indoor Localization Methods Leveraging Smartphone Sensors and Spatial Context.

As Location-Based Services (LBSs) rapidly develop, indoor localization technology is garnering significant attention as a critical component. Smartphones have become tools for indoor localization due to their highly integrated sensors, fast-evolving computational capabilities, and widespread user adoption. With the rapid advancement of smartphones, methods for smartphone-based indoor localization have increasingly attracted attention. Although there are reviews on indoor localization, there is still a lack of systematic reviews focused on smartphone-based indoor localization methods. In particular, existing reviews have not systematically analyzed smartphone-based indoor localization methods or considered the combination of smartphone sensor data with prior knowledge of the indoor environment to enhance localization performance. In this study, through systematic retrieval and analysis, the existing research was first categorized into three types to dissect the strengths and weaknesses based on the types of data sources integrated, i.e., single sensor data sources, multi-sensor data fusion, and the combination of spatial context with sensor data. Then, four key issues are discussed and the research gaps in this field are summarized. Finally, a comprehensive conclusion is provided. This paper offers a systematic reference for research and technological applications related to smartphone-based indoor localization methods.

Ultrasonic Array-Based Multi-Source Fusion Indoor Positioning Technology

Underground mining involves numerous risks, such as collapses, gas leaks, and explosions, posing significant threats to worker safety. In this work, we develop an indoor localization system that uses Bluetooth for coarse positioning and ultrasonic arrays for precision calibration. This system is particularly useful for automated mining operations in underground environments where satellite positioning signals are unavailable. The indoor localization system consists of ultrasonic receiver arrays and an improved multi-transmitter-multi-receiver algorithm, enabling accurate localization within the mining environment. Geometric Dilution of Precision (GDOP) analysis is incorporated to optimize the network layout, and an inertial navigation module is integrated to track the posture of moving objects, enabling precise trajectory determination over large areas, such as coal mines. In the experiment, three traditional methods were compared, and the proposed tracking approach demonstrated a positioning accuracy within 10 cm, reducing error by 20% compared to conventional techniques. This high-precision indoor localization method proves beneficial for underground mining applications.

Global Indoor Localization Method for AGV Based on Ultrasonic Asynchronous Distance Measurement

Abstract In order to realize free path indoor navigation and meet requirements of low cost, an integrated kinematics model of AGV and orienting mechanism of ultrasonic transmitter is established, and a global localization scheme based on three times asynchronous correlated ultrasonic distance measurement and with small number of end-nodes is given. The experiments showed that maximum localization error is just only 67 mm when the AGV travelled at a speed of 0.5 m/s and performed distance measurement at long time intervals of 1 s, which can meet the positioning requirements of most workshop operations.

UWB indoor localization method based on neural network multi-classification for NLOS distance correction

It is well known that ultra-wideband (UWB) is widely used in building indoor positioning systems (IPS) because of its unique advantages. However, compared with the line-of-sight environment (LOS), UWB localization on none-line-of-sight (NLOS) channels has certain limitations, which will reduce the UWB ranging accuracy and location reliability in indoor environment. In this paper, a neural network (NN)-enhanced UWB positioning method is proposed. It can improve positioning performance by using the received channel impulse response (CIR) and UWB raw ranging data to classify the channel conditions and predict the distance. By training CNN-LSTM and MLP neural networks, the proposed method can alleviate the deterioration of localization performance caused by NLOS. The experimental results showed that the average NLOS recognition accuracy of five different obstacles including wooden doors, concrete walls, metal shelves, human body and glass windows reaches up to 92.36 %. In addition, the average root mean square error (RMSE) between the predicted distance and the true distance was 0.3123 m. The indoor positioning test was carried out by weighted least squares (WLS) and the average positioning error under three trajectories was 0.1223 m, which improved the performance by 83.56 % compared with the original UWB positioning system, thus proving its ability to reduce positioning degradation.

Ada-LT IP: Functional Discriminant Analysis of Feature Extraction for Adaptive Long-Term Wi-Fi Indoor Localization in Evolving Environments.

Wi-Fi fingerprint-based indoor localization methods are effective in static environments but encounter challenges in dynamic, real-world scenarios due to evolving fingerprint patterns and feature spaces. This study investigates the temporal variations in signal strength over a 25-month period to enhance adaptive long-term Wi-Fi localization. Key aspects explored include the significance of signal features, the effects of sampling fluctuations, and overall accuracy measured by mean absolute error. Techniques such as mean-based feature selection, principal component analysis (PCA), and functional discriminant analysis (FDA) were employed to analyze signal features. The proposed algorithm, Ada-LT IP, which incorporates data reduction and transfer learning, shows improved accuracy compared to state-of-the-art methods evaluated in the study. Additionally, the study addresses multicollinearity through PCA and covariance analysis, revealing a reduction in computational complexity and enhanced accuracy for the proposed method, thereby providing valuable insights for improving adaptive long-term Wi-Fi indoor localization systems.

Enhanced WiFi/Pedestrian Dead Reckoning Indoor Localization Using Artemisinin Optimization-Particle Swarm Optimization-Particle Filter

WiFi fingerprint-based positioning is a method for indoor localization with the advent of widespread deployment of WiFi and the Internet of Things. However, single WiFi fingerprint positioning has the problems of mismatch, unstable signal strength and limited accuracy. Aiming to address these issues, this paper proposes the fusion algorithm combining WiFi and pedestrian dead reckoning (PDR). Firstly, the particle swarm optimization (PSO) model is utilized to optimize the weighted k-nearest neighbors (WKNN) in the WiFi part. Additionally, the artemisinin optimization (AO) algorithm is used to optimize the particle filter (PF) to improve the fusion effect of the WiFi and PDR. Finally, to thoroughly validate the localization performance of the proposed algorithm, we designed experiments involving two scenarios with four smartphone gestures: calling, dangling, handheld, and pocketed. The experimental results unequivocally indicate that the positioning error of AO-PSO-PF algorithm is lower than that of other algorithms including PDR, WiFi, PF, APF, and FPF. The average positioning errors for the two experiments are 0.95 m and 1.42 m, respectively.

MLA-MFL: A Smartphone Indoor Localization Method for Fusing Multisource Sensors Under Multiple Scene Conditions

MLA-MFL: A Smartphone Indoor Localization Method for Fusing Multisource Sensors Under Multiple Scene Conditions

Multi-sensor fusion for robust indoor localization of industrial UAVs using particle filter

Abstract Robotic platforms including Unmanned Aerial Vehicles (UAVs) require an accurate and reliable source of position information, especially in indoor environments where GNSS cannot be used. This is typically accomplished by using multiple independent position sensors. This paper presents a UAV position estimation mechanism based on a particle filter, that combines information from visual odometry cameras and visual detection of fiducial markers. The article proposes very compact, lightweight and robust method for indoor localization, that can run with high frequency on the UAV’s onboard computer. The filter is implemented such that it can seamlessly handle sensor failures and disconnections. Moreover, the filter can be extended to include inputs from additional sensors. The implemented approach is validated on data from real-life UAV test flights, where average position error under 0.4 m was achieved.

WIO-EKF: Extended Kalman Filtering-Based Wi-Fi and Inertial Odometry Fusion Method for Indoor Localization

WIO-EKF: Extended Kalman Filtering-Based Wi-Fi and Inertial Odometry Fusion Method for Indoor Localization

An improved BIM aided indoor localization method via enhancing cross-domain image retrieval based on deep learning

Aiming at the challenges of reliance on specific hardware, vulnerability to external signal interference, labor-intensive construction of extensive indoor datasets and image blurring due to device motion, an improved Building information modeling (BIM) aided indoor localization method via enhancing cross-domain image retrieval based on deep learning is proposed to achieve a cost-effective, accurate and efficient indoor localization methods in this paper. The proposed indoor localization method includes three processes. Firstly, the NAFNet is employed to remove the motion-blurred images to obtain a more clear and reliable image for image retrieval of indoor localization. Secondly, leverage BIM to create a dataset of rendered images enriched with position and orientation details, addressing labor-intensive construction challenges, reliance on specific hardware, vulnerability to external signal interference in gathering real-world datasets. The BIM rendered images are transformed into realistically styled synthetic images with CycleGAN to narrow the dissimilarity in spanning appearance and texture, facilitating the realism and credibility of cross-domain image retrieval of indoor localization. Lastly, the high adaptability features of synthetic images and indoor photographs are extracted with Swin Transformer, which has multi-scale attention mechanism and location coding prowess. The indoor localization is achieved based on prior position information of BIM rendered images obtained by image retrieval with the cosine similarity measure. The cross-domain image retrieval for BIM aided indoor localization was improved with the deep learning framework of NAFNet-CycleGAN-Swin Transformer. The indoor localization experiments with the public dataset from the University of Melbourne and a self-made dataset from Shandong Jianzhu University demonstrate that the average location accuracy of the proposed method reach of 94 % and 92 %. This advancement significantly enhances the efficiency, accuracy, and practicality of indoor localization, offering novel insights and technical pathways for the implementation of location-based services in the realm of building engineering.

A Performance Comparison between Different Industrial Real-Time Indoor Localization Systems for Mobile Platforms.

The flexibility and versatility associated with autonomous mobile robots (AMR) have facilitated their integration into different types of industries and tasks. However, as the main objective of their implementation on the factory floor is to optimize processes and, consequently, the time associated with them, it is necessary to take into account the environment and congestion to which they are subjected. Localization, on the shop floor and in real time, is an important requirement to optimize the AMRs' trajectory management, thus avoiding livelocks and deadlocks during their movements in partnership with manual forklift operators and logistic trains. Threeof the most commonly used localization techniques in indoor environments (time of flight, angle of arrival, and time difference of arrival), as well as two of the most commonly used indoor localization methods in the industry (ultra-wideband, and ultrasound), are presented and compared in this paper. Furthermore, it identifies and compares three industrial indoor localization solutions: Qorvo, Eliko Kio, and Marvelmind, implemented in an industrial mobile platform, which is the main contribution of this paper. These solutions can be applied to both AMRs and other mobile platforms, such as forklifts and logistic trains. In terms of results, the Marvelmind system, which uses an ultrasound method, was the best solution.

Indoor localisation through Isovist fingerprinting from point clouds and floor plans

ABSTRACT The objective of this paper is to investigate and propose a method for Indoor Localisation based on Isovists, with the aim of extending the fields of Location-based Services and Geomatics. Various methods and combinations incorporating Isovist concepts, Space Syntax, and visibility graphs are examined and assessed. By investigating these approaches, this study aims to create a comprehensive methodology to achieve localisation using Isovists. The main conclusion drawn from this research is that an Indoor Localisation method based on Isovists is not only feasible but can also effectively support Location-based Services. The analysis and evaluation of all the components have been thoroughly conducted, indicating that when properly integrated, they can provide substantial value for LBS applications. As this is a new method for Indoor Localisation, there is significant scope for future work, particularly in terms of connecting it with existing techniques and integrating them into user applications.

Scenario modeling-aided AP placement optimization method for indoor localization and network access

Scenario modeling-aided AP placement optimization method for indoor localization and network access

Attention Mechanism and LSTM Network for Fingerprint-Based Indoor Location System.

The demand for precise indoor localization services is steadily increasing. Among various methods, fingerprint-based indoor localization has become a popular choice due to its exceptional accuracy, cost-effectiveness, and ease of implementation. However, its performance degrades significantly as a result of multipath signal attenuation and environmental changes. In this paper, we propose an indoor localization method based on fingerprints using self-attention and long short-term memory (LSTM). By integrating a self-attention mechanism and LSTM network, the proposed method exhibits outstanding positioning accuracy and robustness in diverse experimental environments. The performance of the proposed method is evaluated under two different experimental scenarios, which involve 2D and 3D moving trajectories, respectively. The experimental results demonstrate that our approach achieves an average localization error of 1.76 m and 2.83 m in the respective scenarios, outperforming the existing state-of-the-art methods by 42.67% and 31.64%.

An Indoor 2-D LiDAR SLAM and Localization Method Based on Artificial Landmark Assistance

An Indoor 2-D LiDAR SLAM and Localization Method Based on Artificial Landmark Assistance

An Efficient Wi-Fi–Vision Map Construction and Self-Maintenance Method for Indoor Localization

An Efficient Wi-Fi–Vision Map Construction and Self-Maintenance Method for Indoor Localization

Method of Real-time Indoor Localization and Monitoring in Parking Lot using Image Deep Learning Analysis

Method of Real-time Indoor Localization and Monitoring in Parking Lot using Image Deep Learning Analysis

Indoor Localization Method Based on Adaptive Wavelet Threshold Analysis

Indoor Localization Method Based on Adaptive Wavelet Threshold Analysis

FILNet: Fast Image-Based Indoor Localization Using an Anchor Control Network.

This paper designs a fast image-based indoor localization method based on an anchor control network (FILNet) to improve localization accuracy and shorten the duration of feature matching. Particularly, two stages are developed for the proposed algorithm. The offline stage is to construct an anchor feature fingerprint database based on the concept of an anchor control network. This introduces detailed surveys to infer anchor features according to the information of control anchors using the visual-inertial odometry (VIO) based on Google ARcore. In addition, an affine invariance enhancement algorithm based on feature multi-angle screening and supplementation is developed to solve the image perspective transformation problem and complete the feature fingerprint database construction. In the online stage, a fast spatial indexing approach is adopted to improve the feature matching speed by searching for active anchors and matching only anchor features around the active anchors. Further, to improve the correct matching rate, a homography matrix filter model is used to verify the correctness of feature matching, and the correct matching points are selected. Extensive experiments in real-world scenarios are performed to evaluate the proposed FILNet. The experimental results show that in terms of affine invariance, compared with the initial local features, FILNet significantly improves the recall of feature matching from 26% to 57% when the angular deviation is less than 60 degrees. In the image feature matching stage, compared with the initial K-D tree algorithm, FILNet significantly improves the efficiency of feature matching, and the average time of the test image dataset is reduced from 30.3 ms to 12.7 ms. In terms of localization accuracy, compared with the benchmark method based on image localization, FILNet significantly improves the localization accuracy, and the percentage of images with a localization error of less than 0.1m increases from 31.61% to 55.89%.