Real-time Indoor Localization Research Articles

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.

Multiple WiFi Access Points Co-Localization Through Joint AoA Estimation

Indoor localization is a fundamental task to many real-world applications, which however remains unresolved, especially with commodity WiFi Access Points (APs). In this paper, we tackle this problem and propose an accurate, robust, and real-time indoor localization system that can be directly deployed on commodity WiFi infrastructure. Specifically, the proposed system makes three key contributions: 1) we introduce a non-parametric metric to measure the accuracy of Angle of Arrival (AoA) estimation; 2) we are the first to explicitly consider the relationship among the AoAs of different APs and propose a multiple APs co-localization algorithm to exploit such a relationship to improve the localization performance; 3) we propose several strategies to reduce the computational complexity of our system to achieve real-time localization. Extensive experiments are conducted to evaluate the performance of the proposed system under various situations, which demonstrate that the proposed system can achieve a 4 degrees median error of AoA estimation and 30 cm localization median error, outperforming the state-of-the-art systems.

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

RSSI-based hybrid algorithm for real-time tracking in underground mining by using RFID technology

Knowing the precise and real-time location of underground mining workers is essential for their health and safety in any emergency. However, the standard Global Positioning System (GPS) is insufficient for such indoor environments as it requires new infrastructure based on different technologies and algorithms. Instead, Radio Frequency Identification (RFID)-based real-time indoor localization systems and a hybrid algorithm are developed. The received-signal-strength (RSS) based positioning techniques are investigated and applied in indoor environments. A unique hybrid approach based on fingerprinting is proposed and developed to solve the disadvantages of the existing techniques. Consequently, the accuracy of this one-of-a-kind algorithm is found to be 2.52 m in an office and 3.13 m in an underground mine. We also compared the proposed hybrid algorithm to the Weighted K-Nearest Neighbor (WKNN). WKNN, on the other hand, has an accuracy of 4.01 m in the office and 4.33 m in underground mining environments.

Real-time indoor localization with visual SLAM for in-building emergency response

Localization is critical to but also challenging for an efficient first response during indoor emergency situations. This paper describes an economical indoor localization system with a low setup cost based on the visual Simultaneous Localization and Mapping (vSLAM). In addition, threshold for key features per image frame is studied for acceptable localization accuracy. The amount of image input users should collect is also analyzed for reliable localization. To address matching and accuracy issues under varying lighting conditions, a comprehensive approach for accurate localization utilizing vSLAM is also proposed. An actual building was tested for the proposed approach with an average localization success rate over 94%.

Outlier-Robust Filtering for Nonlinear Systems With Selective Observations Rejection

Considering a common case where measurements are obtained from independent sensors, we present a novel outlier-robust filter for nonlinear dynamical systems in this work. The proposed method is devised by modifying the measurement model and subsequently using the theory of Variational Bayes and general Gaussian filtering. We treat the measurement outliers independently for independent observations leading to selective rejection of the corrupted data during inference. By carrying out simulations for variable number of sensors we verify that an implementation of the proposed filter is computationally more efficient as compared to the proposed modifications of similar baseline methods still yielding similar estimation quality. In addition, experimentation results for various real-time indoor localization scenarios using Ultra-wide Band (UWB) sensors demonstrate the practical utility of the proposed method.

WeCo-SLAM: Wearable Cooperative SLAM System for Real-Time Indoor Localization Under Challenging Conditions

Real-time globally consistent GPS tracking is critical for an accurate localization and is crucial for applications such as autonomous navigation or multi-robot mapping. However, under challenging environment conditions such as indoor/outdoor transitions, GPS signals are partially available or not consistent over time. In this paper, a real-time tracking system for continuously locating emergency response agents in challenging conditions is presented. A cooperative localization method based on Laser-Visual-Inertial (LVI) and GPS sensors is achieved by communicating optimization events between a LiDAR-Inertial-SLAM (LI-SLAM) and Visual-Inertial-SLAM (VI-SLAM) that operate simultaneously. The estimation of the pose assisted by multiple SLAM approaches provides the GPS localization of the agent when a stand-alone GPS fails. The system has been tested under the terms of the MALIN Challenge, which aims to globally localize agents across outdoor and indoor environments under challenging conditions (such as smoked rooms, stairs, indoor/outdoor transitions, repetitive patterns, extreme lighting changes) where it is well known that a stand-alone SLAM will not be enough to maintaining the localization. The system achieved Absolute Trajectory Error of 0.48%, with a pose update rate between 15 and 20 Hz. Furthermore, the system is able to build a global consistent 3D LiDAR Map that is post-processed to create a 3D reconstruction at different level of details.

Automated workflow analysis in vegetable grafting using an Ultra-Wide Band based real-time indoor location tracking system

• Automatically extracting information for workflow analysis from raw sensory data. • Developing a framework for workflow analysis using Ultra-Wide Band technology. • Identifying worker and task associations based on the location data. • Automatically calculating flow time, processing time, and waiting time from Gantt charts. • Achieving 90.54% accuracy in validating the proposed algorithms against the video data. Traditional workflow analysis requires intensive manual processes to extract meaningful information from raw sensory data. A novel framework for workflow analysis is developed based on a real-time indoor location tracking system utilizing Ultra-Wide Band technology. The proposed framework is applied to identify worker and task associations in a labor-intensive horticultural plant production process for grafted vegetable seedlings, based on location data. It involves binary segmentation of different distance measures calculated from location data to detect the plant tray’s movements and worker association in the vegetable grafting operation. An iterative overlap time removal algorithm is then implemented to ensure that no worker is assigned to overlapping processes, and no task is processed by multiple workers simultaneously. Key workflow metrices such as flow time, processing time, and waiting times are automatically calculated. Automatically generated Gantt charts and bar plots provide valuable insights into the workflow for management and help them identify improvement opportunities. Validation of results with video data show that the proposed workflow analysis is 90.5% accurate.

EdgeLoc: A Robust and Real-Time Localization System Toward Heterogeneous IoT Devices

Indoor localization has become an essential demand driven by indoor location-based services (ILBSs) for mobile users. With the rising of Internet of Things (IoT), heterogeneous smartphones and wearables have become ubiquitous. However, the ILBSs for heterogeneous IoT devices confront significant challenges, such as received signal strength (RSS) variances caused by hardware heterogeneity, multipath reflections from complex environments, and localization time restricted by computation resources. This article proposes EdgeLoc, a robust and real-time indoor localization system toward heterogeneous IoT devices to solve the above challenges. In particular, the RSS fingerprinting data of Wi-Fi is employed for localization and tackling the heterogeneity of IoT devices in twofold. First, feature-level and signal-level solutions are presented to address the random RSS variances. At the feature level, this work proposes a novel capsule neural network model to efficiently extract incremental features from RSS fingerprinting data. At the signal level, a multistep dataflow is further devised to process RSS fingerprints into image-like data, which utilizes the feature matrix to reduce absolute sensing errors introduced by hardware heterogeneity. Second, an edge-IoT framework is designed to utilize the edge server to train the deep learning model and further supports real-time localization for heterogeneous IoT devices. Extensive field experiments with over 33 600 data points are conducted to validate the effectiveness of EdgeLoc with a large-scale Wi-Fi fingerprint data set. The results show that EdgeLoc outperforms the state-of-the-art SAE-CNN method in localization accuracy by up to 14.4%, with an average error of 0.68 m and an average positioning time of 2.05 ms.

A Dynamic HMM-Based Real-Time Location Tracking System Utilizing UHF Passive RFID

In this paper, an indoor real-time location tracking system is proposed using ultra-high frequency passive radio frequency identification technology. A dynamic k nearest neighbor driven hidden Markov model is developed to track subsequent positions of moving objects by combining static localization with dynamic tracking. Statistically validated regression models are developed to relate the tags’ received signal strength indicator and distance, which are iteratively updated over time to accommodate environmental changes. These models are utilized to generate a fingerprint database, which is used by the dynamic k nearest neighbor to estimate the set of potential locations for targeted tagged objects. The probabilistic likelihoods of tags’ potential locations based on the current location and the received signal strength indicator(s) are used to construct the hidden Markov model’s transition and emission matrixes, respectively. Finally, the performance of the proposed framework is evaluated by a set of experiments given different factors, such as movement pattern and speed. Results reveal that our proposed framework can track objects with an average tracking error as low as 0.36m.

Contact Tracing in Healthcare Settings During the COVID-19 Pandemic Using Bluetooth Low Energy and Artificial Intelligence-A Viewpoint.

The COVID-19 pandemic has inflicted great damage with effects that will likely linger for a long time. This crisis has highlighted the importance of contact tracing in healthcare settings because hospitalized patients are among the high risk for complications and death. Moreover, effective contact tracing schemes are not yet available in healthcare settings. A good contact tracing technology in healthcare settings should be equipped with six features: promptness, simplicity, high precision, integration, minimized privacy concerns, and social fairness. One potential solution that addresses all of these elements leverages an indoor real-time location system based on Bluetooth Low Energy and artificial intelligence.

Real-time indoor localization using smartphone magnetic with LSTM networks

Due to the pervasiveness of geomagnetic fields and independence from external infrastructure, localization with smartphone-based magnetic has attracted considerable attention. However, existing approaches are still facing the problem of localization accuracy because of their low discernibility in geomagnetic features. Due to the need to traverse the magnetic database, these approaches cannot obtain localization results in real-time, especially in large indoor localization areas. To address these problems, a novel magnetic indoor localization approach based on long short-term memory networks (LSTMs) is proposed. The magnetic indoor localization is first formulated as a recursive function approximation problem. Based on the analysis of magnetic characteristics, a double sliding window-based dimension expansion approach is designed to generate a time-series magnetic feature dataset. The LSTMs are invoked for magnetic localization on this dataset by taking advantage of its benefits in time-series prediction and characterization. To evaluate the performance of the proposed algorithm, we implement the proposed LSTMs-based magnetic localization approach on a real Android-based smartphone and compare it with fingerprint-based localization algorithms. Extensive experiment results demonstrate the accuracy, response time, and robustness of the proposed algorithm in indoor localization.

A Data Augmentation-Based Method for Robust Device-Free Localization in Changing Environments of Passive Radio Frequency Identification System

Device-free localization (DFL) is playing a critical role in many applications which do not require any device attached to the target. Because of environmental changes, the fingerprint database established in the original environments is unable to remain effective when used in the changing environments. Looking at dropout as a prior-knowledge-free data augmentation operation, we propose a convolutional denoising autoencoder (DAE)-based DFL architecture AugRF. Our method combines the merits of the convolutional neural network and the DAE in which convolutional operation is beneficial to feature extraction and dropout operation is equivalent to generating augmented versions of the training data. It is noted that our method can improve the localization performance in changed environments without resampling and retraining processes. Simulations and real-world experiments verify the superiority of the proposed architecture. Our localization system can be implemented with a commercial ultrahigh-frequency radio frequency identification system. When the signal-to-noise ratio of the fingerprint data drops significantly due to environmental changes, AugRF contributes to improving localization accuracy. In addition, the time cost for one positioning meets the requirement of real-time indoor localization.

High-Accuracy and Real-Time Indoor Positioning System Based on Visible Light Communication and Mobile Robot

For mobile robots and location-based services, precise and real-time positioning is one of the most basic capability, and low-cost positioning solutions are increasingly in demand and have broad market potential. In this paper, we innovatively design a high-accuracy and real-time indoor localization system based on visible light positioning (VLP) and mobile robot. First of all, we design smart LED lamps with VLC and Bluetooth control functions for positioning. The design of LED lamps includes hardware design and Bluetooth control. Furthermore, founded on the loose coupling characteristics of ROS (Robot Operator System), we design a VLP-based robot system with VLP information transmitted by designed LED, dynamic tracking algorithm of high robustness, LED-ID recognition algorithm, and triple-light positioning algorithm. We implemented the VLP-based robot positioning system on ROS in an office equipped with the designed LED lamps, which can realize cm-level positioning accuracy of 3.231 cm and support the moving speed up to 20 km/h approximately. This paper pushes forward the development of VLP application in indoor robots, showing the great potential of VLP for indoor robot positioning.

Indoor Localization Based on Infrared Angle of Arrival Sensor Network.

Accurate, inexpensive, and reliable real-time indoor localization holds the key to the full potential of the context-aware applications and location-based Internet of Things (IoT) services. State-of-the-art indoor localization systems are coping with the complex non-line-of-sight (NLOS) signal propagation which hinders the use of proven multiangulation and multilateration methods, as well as with prohibitive installation costs, computational demands, and energy requirements. In this paper, we present a novel sensor utilizing low-range infrared (IR) signal in the line-of-sight (LOS) context providing high precision angle-of-arrival (AoA) estimation. The proposed sensor is used in the pragmatic solution to the localization problem that avoids NLOS propagation issues by exploiting the powerful concept of the wireless sensor network (WSN). To demonstrate the proposed solution, we applied it in the challenging context of the supermarket cart navigation. In this specific use case, a proof-of-concept navigation system was implemented with the following components: IR-AoA sensor prototype and the corresponding WSN used for cart localization, server-side application programming interface (API), and client application suite consisting of smartphone and smartwatch applications. The localization performance of the proposed solution was assessed in, altogether, four evaluation procedures, including both empirical and simulation settings. The evaluation outcomes are ranging from centimeter-level accuracy achieved in static-1D context up to 1 m mean localization error obtained for a mobile cart moving at 140 cm/s in a 2D setup. These results show that, for the supermarket context, appropriate localization accuracy can be achieved, along with the real-time navigation support, using readily available IR technology with inexpensive hardware components.

Real-Time Indoor Localization for Smartphones Using Tensor-Generative Adversarial Nets.

High-accuracy location awareness in indoor environments is fundamentally important for mobile computing and mobile social networks. However, accurate radio frequency (RF) fingerprint-based localization is challenging due to real-time response requirements, limited RF fingerprint samples, and limited device storage. In this article, we propose a tensor generative adversarial net (Tensor-GAN) scheme for real-time indoor localization, which achieves improvements in terms of localization accuracy and storage consumption. First, with verification on real-world fingerprint data set, we model RF fingerprints as a 3-D low-tubal-rank tensor to effectively capture the multidimensional latent structures. Second, we propose a novel Tensor-GAN that is a three-player game among a regressor, a generator, and a discriminator. We design a tensor completion algorithm for the tubal-sampling pattern as the generator that produces new RF fingerprints as training samples, and the regressor estimates locations for RF fingerprints. Finally, on real-world fingerprint data set, we show that the proposed Tensor-GAN scheme improves localization accuracy from 0.42 m (state-of-the-art methods kNN, DeepFi, and AutoEncoder) to 0.19 m for 80% of 1639 random testing points. Moreover, we implement a prototype Tensor-GAN that is downloaded as an Android smartphone App, which has a relatively small memory footprint, i.e., 57 KB.

Passive Radio-Frequency Identification Tag-Based Indoor Localization in Multi-Stacking Racks for Warehousing

Radio-frequency identification (RFID) technology-based real-time indoor location awareness has been widely studied. In this paper, a passive RFID-based indoor inventory localization method for small and medium-sized enterprises (SMEs) is proposed to effectively manage their indoor inventory tracking in terms of the multi-stacking racking (MSR). To achieve this, we introduce a concept of reference tags and a calculation of measurement for the distance between the RFID reader and reference tag to improve the accuracy of the item location recognition. To illustrate the efficacy and applicability of the method, an empirical case study that applies it to an electronic device manufacturing company is conducted. It was noted that there was no significant difference in the location awareness rate of the proposed system compared with the existing active RFID-based methods. Also, it is demonstrated that the construction can be relatively inexpensive in terms of identifying the location of the items loaded in MSR and relatively narrow areas using a passive tag. This advantage makes it suitable for SMEs that have issues with large-scale facility investment, applying the method to compare the location difference between the registered location information in the inventory system and the actual location of the item in the rack.

Development of a real-time indoor location system using bluetooth low energy technology and deep learning to facilitate clinical applications.

An indoor, real-time location system (RTLS) can benefit both hospitals and patients by improving clinical efficiency through data-driven optimization of procedures. Bluetooth-based RTLS systems are cost-effective but lack accuracy because Bluetooth signal is subject to significant fluctuation. We aim to improve the accuracy of RTLS using the deep learning technique. We installed a Bluetooth sensor network in a three-floor clinic building to track patients, staff, and devices. The Bluetooth sensors measured the strength of the signal broadcasted from Bluetooth tags, which was fed into a deep neural network to calculate the location of the tags. The proposed deep neural network consists of a long short-term memory (LSTM) network and a deep classifier for tracking moving objects. Additionally, a spatial-temporal constraint algorithm was implemented to further increase the accuracy and stability of the results. To train the neural network, we divided the building into 115 zones and collected training data in each zone. We further augmented the training data to generate cross-zone trajectories, mimicking the real-world scenarios. We tuned the parameters for the proposed neural network to achieve relatively good accuracy. The proposed deep neural network achieved an overall accuracy of about 97% for tracking objects in each individual zone in the whole three-floor building, 1.5% higher than the baseline neural network that was proposed in an earlier paper, when using 10s of signals. The accuracy increased with the density of Bluetooth sensors. For tracking moving objects, the proposed neural network achieved stable and accurate results. When latency is less of a concern, we eliminated the effect of latency from the accuracy and gained an accuracy of 100% for our testing trajectories, significantly improved from the baseline method. The proposed deep neural network composed of a LSTM, a deep classifier and a posterior constraint algorithm significantly improved the accuracy and stability of RTLS for tracking moving objects.

MRLIHT: Mobile RFID-based Localization for Indoor Human Tracking.

Radio Frequency Identification (RFID) technology has been widely used in indoor location tracking, especially serving human beings, due to its advantage of low cost, non-contact communication, resistance to hostile environments and so forth. Over the years, many indoor location tracking methods have been proposed. However, tracking mobile RFID readers in real-time has been a daunting task, especially for achieving high localization accuracy. In this paper, we propose a new Mobile RFID (M-RFID)-based Localization approach for Indoor Human Tracking, named MRLIHT. Based on the M-RFID model where RFID readers are equipped on the moving objects (human beings) and RFID tags are fixed deployed in the monitoring area, MRLIHT implements the real-time indoor location tracking effectively and economically. First, based on the readings of multiple tags detected by an RFID reader simultaneously, MRLIHT generates the response regions of tags to the reader. Next, MRLIHT determines the potential location region of the reader where two algorithms are devised. Finally, MRLIHT estimates the location of the reader by dividing the potential location region of the reader into finer-grained grids. The experimental results demonstrate that the proposed MRLIHT performs well in both accuracy and scalability.

Logistics sustainability practices: an IoT-enabled smart indoor parking system for industrial hazardous chemical vehicles

Logistics sustainability practices in industrial cases gain more attention recently especially when transportation efficiency becomes a bottleneck. The research of smart parking develops rapidly especially the thriving of Internet of Things (IoT). In this research, the industrial hazardous chemical vehicle (IHCV) consists of tractor and trailer. The vehicle coupling and decoupling occur frequently in order to fulfil logistics missions. The real-time dynamic indoor location information of both tractors and trailers are of great significance among users. Excessive time and human effort consumed in locating the vehicles lead to the transportation delay and disorderly parking exacerbate congestion inside the indoor parking garage. In this paper, we propose an IoT-enabled smart indoor parking system for logistics vehicles. A self-learning genetic tracking algorithm is developed to ensure the tracking performance. The feasibility and effectiveness of this solution architecture and algorithm are verified in a real-life chemical logistics company. The results show that the proposed algorithm not only performs constant improving location accuracy up to 96.7% after learning but also ensure the long-term use compared to the triangulation method. Moreover, disorderly parking can be identified by location cell partition as to eliminate potential risks. Improved logistics efficiency and lowered congestion situation contribute to the sustainable logistics.