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Selected Papers from UPINLBS 2018

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (31 March 2019) | Viewed by 42469

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Prof. Dr. Ruizhi Chen

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1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
2. Collaborative Innovation Center of Geospatial Technology (INNOGST), Wuhan 430079, China
Interests: Positioning and Navigation with GNSS; Smartphone positioning indoors/outdoors; context awareness and satellite navigation

Prof. Dr. Günther Retscher

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Guest Editor

Engineering Geodesy, Institute of Geodesy and Geophysics, Vienna University of Technology, 1040 Vienna, Austria
Interests: Positioning and Navigation with GNSS; Location Based Services (LBS); Indoor and Pedestrian Navigation; Applications of Multi-sensor Systems
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Xiaoji Niu

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GNSS Research Center of Wuhan University, Wuhan 430079, China;
National Engineering Center for Satellite Positioning System
Interests: High precision GNSS/INS Position and Orientation System; GNSS/INS deep integration; MEMS GNSS/INS integration technologies for land vehicles and UAV; Inertial Surveying

Prof. Dr. Liang Chen

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Guest Editor

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China;
Collaborative Innovation Center of Geospatial Technology (INNOGST), Wuhan 430079, China
Interests: Positioning and Navigation; signal processing and information fusion; Location Based Services (LBS); Indoor and Pedestrian Navigation; Applications of Multi-sensor Systems

Dr. Yuanjin Pan

E-Mail Website
Guest Editor

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Interests: GNSS processing; Geodynamics of earth science; Indoor Positioning

Special Issue Information

Dear Colleagues,

A selection of high quality works presented in the fifth international UPINLBS (Ubiquitous positioning, Indoor Navigation and Location-Based Services) conference will be recommended to the Special Issue of the Journal MDPI Sensors: ‘Selected Papers from UPINLBS 2018 Conference’. The UPINLBS is a unique forum for scientific and technological exchange among global researchers in the field of sensors for indoor/outdoor navigation and positioning. This Conference has a long tradition starting from the year 2010 in Finland and has been held every two years in different cities in the world: Masala, Finland (2010), Helsinki, Finland (2012), Texas, USA (2014), and Shanghai, China (2016). In 2018, the fifth UPINLBS will be held in Wuhan, China (http://unsc.whu.edu.cn/upinlbs/).

This conference concentrate on innovative, state-of-the-art solutions and techniques dealing with ubiquitous positioning, indoor navigation and location-based technologies. In addition, it covers sessions on GNSS based positioning indoors/outdoors, Positioning based on wireless sensor networks, Positioning based on signals of opportunity, Indoor navigation and so much more. Steady progress and the increasing interest in the localization-based services and indoor navigation has contributed to promote the potential of our community in this field, which constitutes one of the most significant and fast developing scientific activities on a worldwide scale.

We invite investigators to contribute original research articles, as well as review articles, to this Special Issue. Potential topics include, but are not limited to:

GNSS based positioning indoors/outdoors
Positioning based on wireless sensor networks
Positioning based on signals of opportunity
Positioning based on acoustic signals
Positioning based on emerging sensors
Hybrid positioning solutions with multiple sensors
Indoor navigation
Indoor mapping
Location-based services indoors/outdoors
Mobile sensing with smartphones/wearable devices
Geospatial data crowdsourcing
Mobile GIS
Context inference and awareness

Prof. Dr. Ruizhi Chen
Prof. Dr. Guenther Guenther Retscher
Prof. Dr. Xiaoji Niu
Prof. Dr. Liang Liang Chen
Dr. Yuanjin Pan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (8 papers)

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Research

16 pages, 7851 KiB

Open AccessArticle

Design of a Smartphone Indoor Positioning Dynamic Ground Truth Reference System Using Robust Visual Encoded Targets

by Xuan Liao, Ruizhi Chen, Ming Li, Bingxuan Guo, Xiaoji Niu and Weilong Zhang

Sensors 2019, 19(5), 1261; https://doi.org/10.3390/s19051261 - 12 Mar 2019

Cited by 8 | Viewed by 3774

Abstract

Smartphone indoor positioning ground truth is difficult to directly, dynamically, and precisely measure in real-time. To solve this problem, this paper proposes and implements a robust smartphone high-precision indoor positioning dynamic real-time ground truth reference system using color visual scatter-encoded targets based on machine vision and photogrammetry. First, a kind of novel high-precision color vision scatter-encoded patterns with a robust recognition rate is designed. Then we use a smartphone to obtain a sequence of images of an experimental room and extract the base points of the color visual scatter-encoded patterns from the sequence images to establish the indoor local coordinate system of the encoded targets. Finally, we use a high-efficiency algorithm to decode the targets of a real-time dynamic shooting image to obtain accurate instantaneous pose information of a smartphone camera and establish the high-precision and high-availability smartphone indoor positioning direct ground truth reference system for preliminary real-time accuracy evaluation of other smartphone positioning technologies. The experimental results show that the encoded targets of the color visual scatter-encoded pattern designed in this paper are easy to detect and identify, and the layout is simple and affordable. It can accurately and quickly solve the dynamic instantaneous pose of a smartphone camera to complete the self-positioning of the smartphone according to the artificial scatter feature visual positioning technology. It is a fast, efficient and low-cost accuracy-evaluation method for smartphone indoor positioning. Full article

(This article belongs to the Special Issue Selected Papers from UPINLBS 2018)

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15 pages, 345 KiB

Open AccessArticle

Smartphone-Based Activity Recognition for Indoor Localization Using a Convolutional Neural Network

by Baoding Zhou, Jun Yang and Qingquan Li

Sensors 2019, 19(3), 621; https://doi.org/10.3390/s19030621 - 01 Feb 2019

Cited by 57 | Viewed by 5825

Abstract

In the indoor environment, the activity of the pedestrian can reflect some semantic information. These activities can be used as the landmarks for indoor localization. In this paper, we propose a pedestrian activities recognition method based on a convolutional neural network. A new convolutional neural network has been designed to learn the proper features automatically. Experiments show that the proposed method achieves approximately 98% accuracy in about 2 s in identifying nine types of activities, including still, walk, upstairs, up elevator, up escalator, down elevator, down escalator, downstairs and turning. Moreover, we have built a pedestrian activity database, which contains more than 6 GB of data of accelerometers, magnetometers, gyroscopes and barometers collected with various types of smartphones. We will make it public to contribute to academic research. Full article

(This article belongs to the Special Issue Selected Papers from UPINLBS 2018)

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24 pages, 9642 KiB

Open AccessArticle

An Infrastructure-Free Indoor Localization Algorithm for Smartphones

by Qu Wang, Haiyong Luo, Aidong Men, Fang Zhao and Yan Huang

Sensors 2018, 18(10), 3317; https://doi.org/10.3390/s18103317 - 03 Oct 2018

Cited by 19 | Viewed by 3881

Abstract

Accurate indoor positioning technology provides location-based service for a variety of applications. However, most existing indoor localization approaches (e.g., Wi-Fi and Bluetooth-based methods) rely heavily on positioning infrastructure, which prevents their large-scale deployment and limits the range at which they are applicable. Here, we proposed an infrastructure-free indoor positioning and tracking approach, termed LiMag, which used ubiquitous magnetic field and ambient lights (e.g., fluorescent, incandescent, and light-emitting diodes (LEDs)) without containing modulated information. We conducted an in-depth study on both the advantages and the challenges in leveraging magnetic field and ambient light intensity for indoor localization. Based on the insights from this study, we established a hybrid observation model that took full advantage of both the magnetic field and ambient light signals. To address the low discernibility of the hybrid observation model, LiMag first generated a single-step fingerprint model by vectorizing consecutive hybrid observations within each step. In order to accurately track users, a lightweight single-step tracking algorithm based on the single-step fingerprints and the particle filter framework was designed. LiMag leveraged the walking information of users and several single-step fingerprints to generate long trajectory fingerprints that exhibited much higher location differentiation ability than the single-step fingerprint. To accelerate particle convergence and eliminate the accumulative error of single-step tracking algorithm, a long trajectory calibration scheme based on long trajectory fingerprints was also introduced. An undirected weighted graph model was constructed to decrease the computational overhead resulting from this long trajectory matching. In addition to typical indoor scenarios including offices, shopping malls and parking lots, we also conducted experiments in more challenging scenarios, including large open-plan areas as well as environments characterized by strong sunlight. Our proposed algorithm achieved a 75th percentile localization accuracy of 1.8 m and 2.2 m, respectively, in the office and shopping mall tested. In conclusion, our LiMag algorithm provided location-based service of infrastructure-free with significantly improved localization accuracy and coverage, as well as satisfactory robustness inside complex indoor environments. Full article

(This article belongs to the Special Issue Selected Papers from UPINLBS 2018)

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25 pages, 6784 KiB

Open AccessArticle

The Accuracy Comparison of Three Simultaneous Localization and Mapping (SLAM)-Based Indoor Mapping Technologies

by Yuwei Chen, Jian Tang, Changhui Jiang, Lingli Zhu, Matti Lehtomäki, Harri Kaartinen, Risto Kaijaluoto, Yiwu Wang, Juha Hyyppä, Hannu Hyyppä, Hui Zhou, Ling Pei and Ruizhi Chen

Sensors 2018, 18(10), 3228; https://doi.org/10.3390/s18103228 - 25 Sep 2018

Cited by 79 | Viewed by 9464

Abstract

The growing interest and the market for indoor Location Based Service (LBS) have been drivers for a huge demand for building data and reconstructing and updating of indoor maps in recent years. The traditional static surveying and mapping methods can’t meet the requirements for accuracy, efficiency and productivity in a complicated indoor environment. Utilizing a Simultaneous Localization and Mapping (SLAM)-based mapping system with ranging and/or camera sensors providing point cloud data for the maps is an auspicious alternative to solve such challenges. There are various kinds of implementations with different sensors, for instance LiDAR, depth cameras, event cameras, etc. Due to the different budgets, the hardware investments and the accuracy requirements of indoor maps are diverse. However, limited studies on evaluation of these mapping systems are available to offer a guideline of appropriate hardware selection. In this paper we try to characterize them and provide some extensive references for SLAM or mapping system selection for different applications. Two different indoor scenes (a L shaped corridor and an open style library) were selected to review and compare three different mapping systems, namely: (1) a commercial Matterport system equipped with depth cameras; (2) SLAMMER: a high accuracy small footprint LiDAR with a fusion of hector-slam and graph-slam approaches; and (3) NAVIS: a low-cost large footprint LiDAR with Improved Maximum Likelihood Estimation (IMLE) algorithm developed by the Finnish Geospatial Research Institute (FGI). Firstly, an L shaped corridor (2nd floor of FGI) with approximately 80 m length was selected as the testing field for Matterport testing. Due to the lack of quantitative evaluation of Matterport indoor mapping performance, we attempted to characterize the pros and cons of the system by carrying out six field tests with different settings. The results showed that the mapping trajectory would influence the final mapping results and therefore, there was optimal Matterport configuration for better indoor mapping results. Secondly, a medium-size indoor environment (the FGI open library) was selected for evaluation of the mapping accuracy of these three indoor mapping technologies: SLAMMER, NAVIS and Matterport. Indoor referenced maps were collected with a small footprint Terrestrial Laser Scanner (TLS) and using spherical registration targets. The 2D indoor maps generated by these three mapping technologies were assessed by comparing them with the reference 2D map for accuracy evaluation; two feature selection methods were also utilized for the evaluation: interactive selection and minimum bounding rectangles (MBRs) selection. The mapping RMS errors of SLAMMER, NAVIS and Matterport were 2.0 cm, 3.9 cm and 4.4 cm, respectively, for the interactively selected features, and the corresponding values using MBR features were 1.7 cm, 3.2 cm and 4.7 cm. The corresponding detection rates for the feature points were 100%, 98.9%, 92.3% for the interactive selected features and 100%, 97.3% and 94.7% for the automated processing. The results indicated that the accuracy of all the evaluated systems could generate indoor map at centimeter-level, but also variation of the density and quality of collected point clouds determined the applicability of a system into a specific LBS. Full article

(This article belongs to the Special Issue Selected Papers from UPINLBS 2018)

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17 pages, 2032 KiB

Open AccessArticle

An Adaptive Weighted KNN Positioning Method Based on Omnidirectional Fingerprint Database and Twice Affinity Propagation Clustering

by Jingxue Bi, Yunjia Wang, Xin Li, Hongxia Qi, Hongji Cao and Shenglei Xu

Sensors 2018, 18(8), 2502; https://doi.org/10.3390/s18082502 - 01 Aug 2018

Cited by 31 | Viewed by 3618

Abstract

The human body has a great influence on Wi-Fi signal power. A fixed K value leads to localization errors for the K-nearest neighbor (KNN) algorithm. To address these problems, we present an adaptive weighted KNN positioning method based on an omnidirectional fingerprint database (ODFD) and twice affinity propagation clustering. Firstly, an OFPD is proposed to alleviate body’s sheltering impact on signal, which includes position, orientation and the sequence of mean received signal strength (RSS) at each reference point (RP). Secondly, affinity propagation clustering (APC) algorithm is introduced on the offline stage based on the fusion of signal-domain distance and position-domain distance. Finally, adaptive weighted KNN algorithm based on APC is proposed for estimating user’s position during online stage. K initial RPs can be obtained by KNN, then they are clustered by APC algorithm based on their position-domain distances. The most probable sub-cluster is reserved by the comparison of RPs’ number and signal-domain distance between sub-cluster center and the online RSS readings. The weighted average coordinates in the remaining sub-cluster can be estimated. We have implemented the proposed method with the mean error of 2.2 m, the root mean square error of 1.5 m. Experimental results show that our proposed method outperforms traditional fingerprinting methods. Full article

(This article belongs to the Special Issue Selected Papers from UPINLBS 2018)

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22 pages, 4984 KiB

Open AccessArticle

A Novel Carrier Loop Algorithm Based on Maximum Likelihood Estimation (MLE) and Kalman Filter (KF) for Weak TC-OFDM Signals

by Wen Liu, Xinmei Bian, Zhongliang Deng, Jun Mo and Buyun Jia

Sensors 2018, 18(7), 2256; https://doi.org/10.3390/s18072256 - 13 Jul 2018

Cited by 8 | Viewed by 3773

Abstract

Digital broadcasting signals represent a promising positioning signal for indoors applications. A novel positioning technology named Time & Code Division-Orthogonal Frequency Division Multiplexing (TC-OFDM) is mainly discussed in this paper, which is based on China mobile multimedia broadcasting (CMMB). Signal strength is an important factor that affects the carrier loop performance of the TC-OFDM receiver. In the case of weak TC-OFDM signals, the current carrier loop algorithm has large residual carrier errors, which limit the tracking sensitivity of the existing carrier loop in complex indoor environments. This paper proposes a novel carrier loop algorithm based on Maximum Likelihood Estimation (MLE) and Kalman Filter (KF) to solve the above problem. The discriminator of the current carrier loop is replaced by the MLE discriminator function in the proposed algorithm. The Levenberg-Marquardt (LM) algorithm is utilized to obtain the MLE cost function consisting of signal amplitude, residual carrier frequency and carrier phase, and the MLE discriminator function is derived from the corresponding MLE cost function. The KF is used to smooth the MLE discriminator function results, which takes the carrier phase estimation, the angular frequency estimation and the angular frequency rate as the state vector. Theoretical analysis and simulation results show that the proposed algorithm can improve the tracking sensitivity of the TC-OFDM receiver by taking full advantage of the characteristics of the carrier loop parameters. Compared with the current carrier loop algorithms, the tracking sensitivity is effectively improved by 2–4 dB, and the better performance of the proposed algorithm is verified in the real environment. Full article

(This article belongs to the Special Issue Selected Papers from UPINLBS 2018)

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17 pages, 7016 KiB

Open AccessArticle

An Indoor Positioning System Based on Static Objects in Large Indoor Scenes by Using Smartphone Cameras

by Aoran Xiao, Ruizhi Chen, Deren Li, Yujin Chen and Dewen Wu

Sensors 2018, 18(7), 2229; https://doi.org/10.3390/s18072229 - 11 Jul 2018

Cited by 50 | Viewed by 6133

Abstract

The demand for location-based services (LBS) in large indoor spaces, such as airports, shopping malls, museums and libraries, has been increasing in recent years. However, there is still no fully applicable solution for indoor positioning and navigation like Global Navigation Satellite System (GNSS) solutions in outdoor environments. Positioning in indoor scenes by using smartphone cameras has its own advantages: no additional needed infrastructure, low cost and a large potential market due to the popularity of smartphones, etc. However, existing methods or systems based on smartphone cameras and visual algorithms have their own limitations when implemented in relatively large indoor spaces. To deal with this problem, we designed an indoor positioning system to locate users in large indoor scenes. The system uses common static objects as references, e.g., doors and windows, to locate users. By using smartphone cameras, our proposed system is able to detect static objects in large indoor spaces and then calculate the smartphones’ position to locate users. The system integrates algorithms of deep learning and computer vision. Its cost is low because it does not require additional infrastructure. Experiments in an art museum with a complicated visual environment suggest that this method is able to achieve positioning accuracy within 1 m. Full article

(This article belongs to the Special Issue Selected Papers from UPINLBS 2018)

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18 pages, 7385 KiB

Open AccessArticle

Unsupervised Indoor Localization Based on Smartphone Sensors, iBeacon and Wi-Fi

by Jing Chen, Yi Zhang and Wei Xue

Sensors 2018, 18(5), 1378; https://doi.org/10.3390/s18051378 - 28 Apr 2018

Cited by 33 | Viewed by 4781

Abstract

In this paper, we propose UILoc, an unsupervised indoor localization scheme that uses a combination of smartphone sensors, iBeacons and Wi-Fi fingerprints for reliable and accurate indoor localization with zero labor cost. Firstly, compared with the fingerprint-based method, the UILoc system can build a fingerprint database automatically without any site survey and the database will be applied in the fingerprint localization algorithm. Secondly, since the initial position is vital to the system, UILoc will provide the basic location estimation through the pedestrian dead reckoning (PDR) method. To provide accurate initial localization, this paper proposes an initial localization module, a weighted fusion algorithm combined with a k-nearest neighbors (KNN) algorithm and a least squares algorithm. In UILoc, we have also designed a reliable model to reduce the landmark correction error. Experimental results show that the UILoc can provide accurate positioning, the average localization error is about 1.1 m in the steady state, and the maximum error is 2.77 m. Full article

(This article belongs to the Special Issue Selected Papers from UPINLBS 2018)

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