Robust Indoor Localization Research Articles

An Algorithm for Accurate and Robust Indoor Localization Based on Nonlinear Programming

The study of techniques to estimate the position of mobile devices with a high level of accuracy and robustness is essential to provide advanced location based services in indoor environments. An algorithm to enable mobile devices to estimate their positions in known indoor environments is proposed in this paper under the assumption that fixed anchor nodes are available at known locations. The proposed algorithm is specifically designed to be executed on the mobile device whose position is under investigation, and it allows the device to estimate its position within the environment by actively measuring distance estimates from the anchor nodes. In order to reduce the impact of the errors caused by the arrangement of the anchor nodes in the environment, the proposed algorithm first transforms the localization problem into an optimization problem, and then, it solves the derived optimization problem using techniques inspired by nonlinear programming. Experimental results obtained using ultra-wide band signaling are presented to assess the performance of the algorithm and to compare it with reference alternatives. The presented experimental results confirm that the proposed algorithm provides an increased level of accuracy and robustness with respect to two reference alternatives, regardless of the position of the anchor nodes.

Smartphone-Based Indoor Localization With Integrated Fingerprint Signal

Indoor localization of smartphones has received much attention recently and the smartphone localization is essential to a wide range of applications in office buildings, nursing homes, parking lots, and other public places. Existing solutions relying on inertial sensors or received signal strength suffer from large location errors and poor stability. We observe an opportunity in the recent trend of increasing numbers of wireless transmitters installed in indoor spaces to design a precise and robust indoor localization solution. We can extract fine-grained channel state information from wireless transmitters for indoor fingerprint localization. However, the accuracy of localization relying on a single physical quantity is limited and difficult to self-correct. This study proposes an integrated channel state information (CSI) and magnetic field strength (MFS) localization method (CSMS) that achieves sub-meter accuracy for smartphones. CSMS constructs an integrated fingerprint map of CSI and MFS and proposes the Local Dynamic Time Warping algorithm for geomagnetic tracking and the Multi-Module Data k-Nearest Neighbor algorithm for fusion fingerprint dynamic weighted comparison. By doing so, CSMS outputs enhanced accuracy with low cost, while overcoming the respective drawbacks of each individual sub-system. We conduct extensive experiments in two scenarios to validate the performance of CSMS. The results of experimental show that the mean distance error in both scenarios is less than 0.5m which is significantly superior to existing smartphone-based indoor positioning methods.

A vision and learning-based indoor localization and semantic mapping framework for facility operations and management

Abstract Recent research on facility management has focused on leveraging location-based services (LBS) to assist on-demand access to model information and on-site documentation. Fast and robust indoor localization is of great importance for location-based facility management services, such as the ones used in mobile computing settings. However, there are several challenges in achieving fast and robust indoor localization: 1) Signal-based indoor localization methods, such as WIFI, RFID, Bluetooth and Ultrasound, require installation of extra infrastructures in a building to support localization; 2) Visual-based indoor localization methods, such as LiDAR and camera, depend on feature point detection and matching, which require heavy computation and can also be impacted by environmental conditions, such as lighting and texture richness. In addition to these, existing localization methods do not support semantic understanding which is of great importance when associating a component with its digital twin. To address the stated problems above, this paper presents a vision and learning-based framework that utilizes a shared convolutional neural network to perform localization and semantic segmentation simultaneously. The proposed framework can support facility management by locating facility components within a building and associate them with their digital twins in an information repository. Compared to conventional methods, the developed image-based indoor localization and semantic mapping framework has the following advantages: 1) It only requires image as input to support localization, semantic understanding, and association, which eliminates the need for extra infrastructure, such as deployment of RFID tags, etc.; 2) It reuses the feature extraction network for simultaneous localization and semantic understanding, which saves computing resources; 3) With 6-DoF poses and semantic labels, it supports component-level association. The authors evaluated the proposed framework on publicly available data sets using three metrics: localization accuracy, semantic segmentation accuracy, and association success rate. The results show that the proposed image-based method can achieve 6-DoF localization and semantic segmentation concurrently. Also, formalized experiments on a synthetic data set with different noise levels introduced to localization and semantic segmentation showed that a main factor affecting the performance of association of an image to its digital twin is the accuracy of its localization.

A Robust Indoor Localization System Integrating Visual Localization Aided by CNN-Based Image Retrieval with Monte Carlo Localization.

This paper proposes a novel multi-sensor-based indoor global localization system integrating visual localization aided by CNN-based image retrieval with a probabilistic localization approach. The global localization system consists of three parts: coarse place recognition, fine localization and re-localization from kidnapping. Coarse place recognition exploits a monocular camera to realize the initial localization based on image retrieval, in which off-the-shelf features extracted from a pre-trained Convolutional Neural Network (CNN) are adopted to determine the candidate locations of the robot. In the fine localization, a laser range finder is equipped to estimate the accurate pose of a mobile robot by means of an adaptive Monte Carlo localization, in which the candidate locations obtained by image retrieval are considered as seeds for initial random sampling. Additionally, to address the problem of robot kidnapping, we present a closed-loop localization mechanism to monitor the state of the robot in real time and make adaptive adjustments when the robot is kidnapped. The closed-loop mechanism effectively exploits the correlation of image sequences to realize the re-localization based on Long-Short Term Memory (LSTM) network. Extensive experiments were conducted and the results indicate that the proposed method not only exhibits great improvement on accuracy and speed, but also can recover from localization failures compared to two conventional localization methods.

Robust Indoor Localization in a Reverberant Environment Using Microphone Pairs and Asynchronous Acoustic Beacons

In this paper, a robust indoor localization method using microphone pairs and asynchronous acoustic beacons was proposed. The proposed method is applicable even with a two-channel microphone pair, which is the minimal configuration of a microphone array. The proposed method estimates location by using the cross-correlation functions of the measured signals as location likelihoods. Three experiments were conducted to evaluate the proposed method. Four beacons were located at the corners of a localizing area of 4 m by 4 m and emitted signals with a bandwidth of 2 kHz. The localization results were compared to the previous method with deterministic direction-of-arrival estimation. The 90th percentiles of the localization error were 0.23 m for the proposed method with two microphones, 0.19 m for the proposed method with four microphones, and 0.30 m for the previous method under conditions without significant reverberation. Under a condition with reflective walls, the 90th percentile of the localization error of the previous method increased to 0.49 m, while that of the proposed method was only increased to 0.23 m for two microphones and 0.19 m for four microphones. The proposed method contributes to a robust localization in indoor environments and relieves the constraints of receiver configuration.

Applying Deep Neural Network (DNN) for Robust Indoor Localization in Multi-Building Environment

In the Internet of Things (IoT) era, indoor localization plays a vital role in academia and industry. Wi-Fi is a promising scheme for indoor localization as it is easy and free of charge, even for private networks. However, Wi-Fi has signal fluctuation problems because of dynamic changes of environments and shadowing effects. In this paper, we propose to use a deep neural network (DNN) to achieve accurate localization in Wi-Fi environments. In the localization process, we primarily construct a database having all reachable received signal strengths (RSSs), and basic service set identifiers (BSSIDs). Secondly, we fill the missed RSS values using regression, and then apply linear discriminant analysis (LDA) to reduce features. Thirdly, the 5-BSSIDs having the strongest RSS values are appended with reduced RSS vector. Finally, a DNN is applied for localizing Wi-Fi users. The proposed system is evaluated in the classification and regression schemes using the python programming language. The results show that 99.15% of the localization accuracy is correctly classified. Moreover, the coordinate-based localization provides 50%, 75%, and 93.10% accuracies for errors less than 0.50 m, 0.75 m, and 0.90 m respectively. The proposed method is compared with other algorithms, and our method provides motivated results. The simulation results also show that the proposed method can robustly localize Wi-Fi users in hierarchical and complex wireless environments.

Robust Indoor Localization across Smartphone Models with Ellipsoid Features from Multiple RSSIs

Localization for mobile devices has become important as the basis technology for various ubiquitous computing applications. While GPS is leveraged as the de-facto standard technology in outdoor localization, its accuracy is poor indoors. For twenty years, researchers have tried to investigate indoor localization technology using fingerprinting from received signal strength indicators (RSSIs). With the widespread use of smartphones in the last decade, device dependency (e.g. antenna characteristics) must be considered to avoid performance degradation, while most of the recent localization approaches assume that all the smartphone models have the same device characteristics. In this paper, we propose a novel feature representation based on multiple RSSIs for compensating performance degradation against smartphone models changes. In contrast to the previous feature representation based on a single RSSI, our new feature representation, which we call Ellipsoid features, employs tuples of pair of RSSIs to eliminate device dependence in the path loss model for wave propagation. In contrast to recent advances in machine learning methods such as domain adaptation, multi-task learning, and semi-supervised learning, our approach requires no additional dataset nor retraining for the new target models. This simplicity would promote ubiquity of indoor localization in the era of smartphones. Moreover, our feature representation works well compared to the state-of-the-arts in feature representations based on multiple RSSIs even when only a small number of access points (APs) are available. Experimental result using smartphone devices including Android Nexus5, Nexus5X, Nexus6P, and Xperia X Performance shows that our approach achieves superior performance over the state-of-the-art indoor localization models as well as robust performance against device changes.

Received signal strength–based indoor localization using a robust interacting multiple model–extended Kalman filter algorithm

Due to the vast increase in location-based services, currently there exists an actual need of robust and reliable indoor localization solutions. Received signal strength localization is widely used due to its simplicity and availability in most mobile devices. The received signal strength channel model is defined by the propagation losses and the shadow fading. In real-life applications, these parameters might vary over time because of changes in the environment. Thus, to obtain a reliable localization solution, they have to be sequentially estimated. In this article, the problem of tracking a mobile node by received signal strength measurements is addressed, simultaneously estimating the model parameters. Particularly, a two-slope path loss model is assumed for the received signal strength observations, which provides a more realistic representation of the propagation channel. The proposed methodology considers a parallel interacting multiple model–based architecture for distance estimation, which is coupled with the on-line estimation of the model parameters and the final position determination via Kalman filtering. Numerical simulation results in realistic scenarios are provided to support the theoretical discussion and to show the enhanced performance of the new robust indoor localization approach. Additionally, experimental results using real data are reported to validate the technique.

Spatial statistical analysis for the design of indoor particle-filter-based localization mechanisms

The accurate localization of end-users and resources is seen as one of the main pillars toward the successful implementation of context-based applications. While current outdoor localization mechanisms fulfill most application requirements, the design of accurate indoor localization mechanisms is still an open issue. Most research efforts are focusing on the design of mechanisms making use of the receiver signal strength indications generated by WLAN (wireless local area network) devices. However, the accuracy and robustness of such mechanisms can be severely compromised due to the random and unpredictable nature of radio channels. In this article, we develop a methodology incorporating various algorithms capable of coping with the unpredictable nature of radio channels. Following a holistic approach, we start by identifying the wireless equipment parameter setting, better meeting the implementation requirements of a robust indoor localization mechanism. We then make use of RANdom SAmple Consensus paradigm: a robust model-fitting mechanism capable of smoothing the data captured during the space survey. Using an experimental setup, we evaluate the benefits of integrating the floor plan and an ordinary Kriging interpolation algorithm in the estimation process. Our main findings show that our proposal can greatly improve the quality of the information to be used in the development of particle-filter-based indoor localization mechanisms.

Locus: robust and calibration-free indoor localization, tracking and navigation for multi-story buildings

A fundamental goal of indoor localisation technology is to achieve the milestone of combining minimal cost with accuracy sufficient enough for general consumer applications. To achieve this, current indoor positioning systems need either extensive calibration or expensive hardware. Moreover, very few systems built so far have addressed floor determination in multi-story buildings. In this paper, we explain a Wi-fi-based indoor localisation, tracking and navigation system for multi-story buildings called Locus. Locus determines a device’s floor as well as location on that floor using existing knowledge of infrastructure, and without requiring any calibration or proprietary hardware. It is an inexpensive solution with minimum set-up and maintenance expenses, is scalable, readily deployable and robust to environmental changes. Experimental results in three different buildings spanning multiple floors show that it can determine the floor with 95.33% accuracy and the location on the floor with an error of 6.49 m on an average in real-life practical environments. We also demonstrate its utility via two location-based applications for indoor navigation and tracking in emergency scenarios.

Robust Indoor Localization on a Commercial Smart Phone

Low-cost localization solutions for indoor environments have a variety of real-world applications ranging from emergency evacuation to mobility aids for people with disabilities. In this paper, we introduce a methodology for indoor localization using a commercial smart-phone combining dead reckoning and Wifi signal strength fingerprinting. Additionally, we outline an automated procedure for collecting Wifi calibration data that uses a robot equipped with a laser rangefinder and fiber optic gyroscope. These measurements along with a generated robot map of the environment are combined using a particle filter towards robust pose estimation. The uniqueness of our approach lies in the implementation of the complementary nature of the solution as well as in the efficient adaptation to the smart-phone platform. The system was tested using multiple participants in two different indoor environments, and achieved localization accuracies on the order of 5 meters; sufficient for a variety of navigation and context-aware applications.

Differential radio map-based robust indoor localization

While wireless local area network-based indoor localization is attractive, the problems concerning how to capture the signal-propagating character in the complex dynamic environment and how to accommodate the receiver gain difference of different mobile devices are challenging. In this article, we solve these problems by modeling them as common mode noise and develop a localization algorithm based on a novel differential radio map approach. We propose a differential operation to improve the performance of the radio map module, where the location is estimated according to the difference of received signal strength (RSS) instead of RSS itself. The particle filter algorithm is adopted to realize the target localization and tracking task. Furthermore, to calculate the particle weight at arbitrary locations, we propose a local linearization technique to realize continuous interpolation of the radio map. The indoor experiment results demonstrate the effectiveness and robustness of our approach.