An Indoor Localization Approach Based on Deep Learning for Indoor Location-Based Services

2020 ◽

Vol XLIII-B4-2020 ◽

pp. 391-397

Author(s):

Y. Yang ◽

C. Toth ◽

D. Brzezinska

Keyword(s):

Deep Learning ◽

High Performance ◽

Large Scale ◽

Indoor Localization ◽

Indoor Positioning ◽

Location Based Services ◽

Smart Devices ◽

Visual Localization ◽

Indoor Location ◽

Semantic Labeling

Abstract. Indoor positioning technologies represent a fast developing field of research due to the rapidly increasing need for indoor location-based services (ILBS); in particular, for applications using personal smart devices. Recently, progress in indoor mapping, including 3D modeling and semantic labeling started to offer benefits to indoor positioning algorithms; mainly, in terms of accuracy. This work presents a method for efficient and robust indoor localization, allowing to support applications in large-scale environments. To achieve high performance, the proposed concept integrates two main indoor localization techniques: Wi-Fi fingerprinting and deep learning-based visual localization using 3D map. The robustness and efficiency of technique is demonstrated with real-world experiences.

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Privacy vs. Reward in Indoor Location-Based Services

Proceedings on Privacy Enhancing Technologies ◽

10.1515/popets-2016-0031 ◽

2016 ◽

Vol 2016 (4) ◽

pp. 102-122 ◽

Cited By ~ 3

Author(s):

Kassem Fawaz ◽

Kyu-Han Kim ◽

Kang G. Shin

Keyword(s):

Location Privacy ◽

Indoor Localization ◽

Service Providers ◽

Location Based Services ◽

Location Information ◽

Privacy Concerns ◽

Indoor Location ◽

Location Data ◽

Personalized Services ◽

Privacy Risks

AbstractWith the advance of indoor localization technology, indoor location-based services (ILBS) are gaining popularity. They, however, accompany privacy concerns. ILBS providers track the users’ mobility to learn more about their behavior, and then provide them with improved and personalized services. Our survey of 200 individuals highlighted their concerns about this tracking for potential leakage of their personal/private traits, but also showed their willingness to accept reduced tracking for improved service. In this paper, we propose PR-LBS (Privacy vs. Reward for Location-Based Service), a system that addresses these seemingly conflicting requirements by balancing the users’ privacy concerns and the benefits of sharing location information in indoor location tracking environments. PR-LBS relies on a novel location-privacy criterion to quantify the privacy risks pertaining to sharing indoor location information. It also employs a repeated play model to ensure that the received service is proportionate to the privacy risk. We implement and evaluate PR-LBS extensively with various real-world user mobility traces. Results show that PR-LBS has low overhead, protects the users’ privacy, and makes a good tradeoff between the quality of service for the users and the utility of shared location data for service providers.

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A Novel Passive Indoor Localization Method by Fusion CSI Amplitude and Phase Information

Sensors ◽

10.3390/s19040875 ◽

2019 ◽

Vol 19 (4) ◽

pp. 875 ◽

Cited By ~ 10

Author(s):

Xiaochao Dang ◽

Xiong Si ◽

Zhanjun Hao ◽

Yaning Huang

Keyword(s):

Indoor Localization ◽

Rapid Development ◽

Location Based Services ◽

Signal Interference ◽

Indoor Environments ◽

Phase Information ◽

Localization Accuracy ◽

Localization Method ◽

Indoor Location ◽

Conference Room

With the rapid development of wireless network technology, wireless passive indoor localization has become an increasingly important technique that is widely used in indoor location-based services. Channel state information (CSI) can provide more detailed and specific subcarrier information, which has gained the attention of researchers and has become an emphasis in indoor localization technology. However, existing research has generally adopted amplitude information for eigenvalue calculations. There are few research studies that have used phase information from CSI signals for localization purposes. To eliminate the signal interference existing in indoor environments, we present a passive human indoor localization method named FapFi, which fuses CSI amplitude and phase information to fully utilize richer signal characteristics to find location. In the offline stage, we filter out redundant values and outliers in the CSI amplitude information and then process the CSI phase information. A fusion method is utilized to store the processed amplitude and phase information as a fingerprint database. The experimental data from two typical laboratory and conference room environments were gathered and analyzed. The extensive experimental results demonstrate that the proposed algorithm is more efficient than other algorithms in data processing and achieves decimeter-level localization accuracy.

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Smartphone Sensor Based Indoor Positioning: Current Status, Opportunities, and Future Challenges

Electronics ◽

10.3390/electronics9060891 ◽

2020 ◽

Vol 9 (6) ◽

pp. 891

Author(s):

Imran Ashraf ◽

Soojung Hur ◽

Yongwan Park

Keyword(s):

Radio Frequency Identification ◽

Indoor Localization ◽

Indoor Positioning ◽

Location Based Services ◽

Ultra Wideband ◽

Current Status ◽

Indoor Location ◽

Use Of Data ◽

Future Challenges ◽

Smartphone Sensors

The last two decades have witnessed a rich variety of indoor positioning and localization research. Starting with Microsoft Research pioneering the fingerprint approach based RADAR, MIT’s Cricket, and then moving towards beacon-based localization are few among many others. In parallel, researchers looked into other appealing and promising technologies like radio frequency identification, ultra-wideband, infrared, and visible light-based systems. However, the proliferation of smartphones over the past few years revolutionized and reshaped indoor localization towards new horizons. The deployment of MEMS sensors in modern smartphones have initiated new opportunities and challenges for the industry and academia alike. Additionally, the demands and potential of location-based services compelled the researchers to look into more robust, accurate, smartphone deployable, and context-aware location sensing. This study presents a comprehensive review of the approaches that make use of data from one or more sensors to estimate the user’s indoor location. By analyzing the approaches leveraged on smartphone sensors, it discusses the associated challenges of such approaches and points out the areas that need considerable research to overcome their limitations.

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VISION-BASED INDOOR LOCALIZATION VIA A VISUAL SLAM APPROACH

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w13-827-2019 ◽

2019 ◽

Vol XLII-2/W13 ◽

pp. 827-833

Author(s):

M. Li ◽

F. Rottensteiner

Keyword(s):

Image Retrieval ◽

Degrees Of Freedom ◽

Feature Matching ◽

Indoor Localization ◽

Location Based Services ◽

Mapping Technique ◽

Visual Slam ◽

Indoor Location ◽

Vocabulary Tree ◽

Localization And Mapping

<p><strong>Abstract.</strong> With an increasing interest in indoor location based services, vision-based indoor localization techniques have attracted many attentions from both academia and industry. Inspired by the development of simultaneous localization and mapping technique (SLAM), we present a visual SLAM-based approach to achieve a 6 degrees of freedom (DoF) pose in indoor environment. Firstly, the indoor scene is explored by a keyframe-based global mapping technique, which generates a database from a sequence of images covering the entire scene. After the exploration, a feature vocabulary tree is trained for accelerating feature matching in the image retrieval phase, and the spatial structures obtained from the keyframes are stored. Instead of querying by a single image, a short sequence of images in the query site are used to extract both features and their relative poses, which is a local visual SLAM procedure. The relative poses of query images provide a pose graph-based geometric constraint which is used to assess the validity of image retrieval results. The final positioning result is obtained by selecting the pose of the first correct corresponding image.</p>

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RETHINKING INDOOR LOCALIZATION SOLUTIONS TOWARDS THE FUTURE OF MOBILE LOCATION-BASED SERVICES

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-4-w4-235-2017 ◽

2017 ◽

Vol IV-4/W4 ◽

pp. 235-247 ◽

Cited By ~ 1

Author(s):

C. Guney

Keyword(s):

Indoor Localization ◽

Smart Cities ◽

Indoor Navigation ◽

Location Based Services ◽

Outdoor Environment ◽

Indoor Environments ◽

Navigation Systems ◽

Positioning Systems ◽

Indoor Location ◽

High Level

Satellite navigation systems with GNSS-enabled devices, such as smartphones, car navigation systems, have changed the way users travel in outdoor environment. GNSS is generally not well suited for indoor location and navigation because of two reasons: First, GNSS does not provide a high level of accuracy although indoor applications need higher accuracies. Secondly, poor coverage of satellite signals for indoor environments decreases its accuracy. So rather than using GNSS satellites within closed environments, existing indoor navigation solutions rely heavily on installed sensor networks. There is a high demand for accurate positioning in wireless networks in GNSS-denied environments. However, current wireless indoor positioning systems cannot satisfy the challenging needs of indoor location-aware applications. Nevertheless, access to a user’s location indoors is increasingly important in the development of context-aware applications that increases business efficiency. In this study, how can the current wireless location sensing systems be tailored and integrated for specific applications, like smart cities/grids/buildings/cars and IoT applications, in GNSS-deprived areas.

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Indoor Localization Using Deep-Learning and Smartphone

Iraqi Journal of Computer Communication Control and System Engineering ◽

10.33103/uot.ijccce.19.3.4 ◽

2019 ◽

pp. 40-49

Keyword(s):

Deep Learning ◽

Real Time ◽

Indoor Localization ◽

Learning Phase ◽

Second Phase ◽

Indoor Location ◽

Online Processing ◽

Offline Learning ◽

Proposed Model ◽

Matching Techniques

Robust and accurate indoor localization has been the goal of several research efforts over the past decade. In the building where the GPS is not available, this project can be utilized. Indoor localization based on image matching techniques related to deep learning was achieved in a hard environment. So, if it wanted to raise the precision of indoor classification, the number of image dataset of the indoor environment should be as large as possible to satisfy and cover the underlying area. In this work, a smartphone camera is used to build the image-based dataset of the investigated building. In addition, captured images in real time are taken to be processed with the proposed model as a test set. The proposed indoor localization includes two phases the first one is the offline learning phase and the second phase is the online processing phase. In the offline learning phase, here we propose a convolutional neural network (CNN) model that sequences the features of image data from some classis's dataset composed with a smartphone camera. In the online processing phase, an image is taken by the camera of a smartphone in real–time to be tested by the proposed model. The obtained results of the prediction can appoint the expected indoor location. The proposed system has been tested over various experiments and the obtained experimental results show that the accuracy of the prediction is almost 98.0%.

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