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Concurrent Positioning, Mapping and Perception of Multi-Source Data Fusion for Smart Applications II

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".

Deadline for manuscript submissions: closed (5 February 2024) | Viewed by 7759

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Special Issue Editors

Prof. Dr. Jay Hyoun Kwon

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Guest Editor

Department of Geoinformatics, University of Seoul, Seoul 02504, Korea
Interests: GNSS; sensor fusion; autonomous navigation
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Chang-Ki Hong

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Guest Editor

Department of Geoinformatics Engineering, Kyungil University, Gyeongsan 38428, Korea
Interests: GNSS; ionospheric modeling; kinematic positioning

Prof. Dr. Tae-Suk Bae

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Department of Geoinformation Engineering, Sejong University, Seoul 05006, Korea
Interests: geodesy; GNSS; orbit determination
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Hung-Kyu Lee

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Guest Editor

Department of Civil Engineering, Changwon National University, Changwon 51140, Korea
Interests: GNSS; sensor integration; Kalman filtering

Special Issue Information

Dear Colleagues,

In the era of the 4th industrial revolution, the importance of positioning is growing at an unprecedented rate. Numerous applications are emerging from science that involve identifying the dynamics of Earth, displacement monitoring of large structures, navigation of autonomous vehicles, and safety and convenience of the public. Positioning is being performed faster and more accurately through various sensors and their fusion. Mapping based on precise location and spatial recognition and interpretation is generating the spatial information needed in the age of artificial intelligence and allowing it to be utilized in various smart applications. All these developments are due to the fusion of simultaneous acquired positioning information and data from multiple sources.

The Special Issue aims at research regarding positioning and mapping from conventional fields. More importantly, it covers the topics addressing how multi-source data can be combined for smart applications. Under the approaching era of AI, the fusion of disparate data sources is a fundamental methodology for instantaneous applications in geosciences, ultimately for the convenience of all creatures on this planet.

Following on from the previous Special Issue, “Concurrent Positioning, Mapping and Perception of Multi-Source Data Fusion for Smart Applications II” invites papers dealing with the following topics of interest (though other topics relevant to the Special Issue theme are also welcome):

Autonomous positioning and localization
State of art mapping and digital twin
Intelligent spatial perception
Simultaneous and reciprocal positioning, mapping and perception of indoor/outdoor environments
Smart mobility systems and applications

Prof. Dr. Jay Hyoun Kwon
Prof. Dr. Chang-Ki Hong
Prof. Dr. Tae-Suk Bae
Prof. Dr. Hung-Kyu Lee
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. Remote Sensing 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 2700 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.

Keywords

GNSS
positioning
sensor
mapping
fusion
multi-source

Published Papers (5 papers)

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Research

20 pages, 2929 KiB

Open AccessArticle

A High-Precision 3D Target Perception Algorithm Based on a Mobile RFID Reader and Double Tags

by Yaqin Xie, Tianyuan Gu, Di Zheng, Yu Zhang and Hai Huan

Remote Sens. 2023, 15(15), 3914; https://doi.org/10.3390/rs15153914 - 07 Aug 2023

Viewed by 887

Abstract

With the popularization of positioning technology, more and more industries have begun to pay attention to the application and demand of location information, and almost all industries can benefit from low-cost and high-precision location information. This paper introduces a novel three-dimensional (3D) low-cost, high-precision target perception algorithm that utilizes a Radio Frequency Identification (RFID) mobile reader and double tags. Initially, the Received Signal Strength (RSS) is employed to estimate the approximate position of the target along the length direction of the shelf. Additionally, double tags are affixed to the target, enabling the perception of its approximate height and depth through phase information measurements. Subsequently, the obtained rough position serves as an initial value for calibration using the proposed algorithm, allowing for the refinement of the target’s length information relative to the shelf. Simulation results demonstrate the exceptional accuracy of the proposed method in perceiving the 3D position information of the target, achieving centimeter-level sensing accuracy. Full article

(This article belongs to the Special Issue Concurrent Positioning, Mapping and Perception of Multi-Source Data Fusion for Smart Applications II)

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19 pages, 5534 KiB

Open AccessArticle

Hybrid Indoor Positioning System Based on Acoustic Ranging and Wi-Fi Fingerprinting under NLOS Environments

by Zhengyan Zhang, Yue Yu, Liang Chen and Ruizhi Chen

Remote Sens. 2023, 15(14), 3520; https://doi.org/10.3390/rs15143520 - 12 Jul 2023

Cited by 2 | Viewed by 1399

Abstract

An accurate indoor positioning system (IPS) for the public has become an essential function with the fast development of smart city-related applications. The performance of the current IPS is limited by the complex indoor environments, the poor performance of smartphone built-in sensors, and time-varying measurement errors of different location sources. This paper introduces a hybrid indoor positioning system (H-IPS) that combines acoustic ranging, Wi-Fi fingerprinting, and low-cost sensors. This system is designed specifically for large-scale indoor environments with non-line-of-sight (NLOS) conditions. To improve the accuracy in estimating pedestrian motion trajectory, a data and model dual-driven (DMDD) model is proposed to integrate the inertial navigation system (INS) mechanization and the deep learning-based speed estimator. Additionally, a double-weighted K-nearest neighbor matching algorithm enhanced the accuracy of Wi-Fi fingerprinting and scene recognition. The detected scene results were then utilized for NLOS detection and estimation of acoustic ranging results. Finally, an adaptive unscented Kalman filter (AUKF) was developed to provide universal positioning performance, which further improved by the Wi-Fi accuracy indicator and acoustic drift estimator. The experimental results demonstrate that the presented H-IPS achieves precise positioning under NLOS scenes, with meter-level accuracy attainable within the coverage range of acoustic signals. Full article

(This article belongs to the Special Issue Concurrent Positioning, Mapping and Perception of Multi-Source Data Fusion for Smart Applications II)

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25 pages, 5643 KiB

Open AccessArticle

Autonomous Multi-Floor Localization Based on Smartphone-Integrated Sensors and Pedestrian Indoor Network

by Chaoyang Shi, Wenxin Teng, Yi Zhang, Yue Yu, Liang Chen, Ruizhi Chen and Qingquan Li

Remote Sens. 2023, 15(11), 2933; https://doi.org/10.3390/rs15112933 - 04 Jun 2023

Cited by 1 | Viewed by 1543

Abstract

Autonomous localization without local wireless facilities is proven as an efficient way for realizing location-based services in complex urban environments. The precision of the current map-matching algorithms is subject to the poor ability of integrated sensor-based trajectory estimation and the efficient combination of pedestrian motion information and the pedestrian indoor network. This paper proposes an autonomous multi-floor localization framework based on smartphone-integrated sensors and pedestrian network matching (ML-ISNM). A robust data and model dual-driven pedestrian trajectory estimator is proposed for accurate integrated sensor-based positioning under different handheld modes and disturbed environments. A bi-directional long short-term memory (Bi-LSTM) network is further applied for floor identification using extracted environmental features and pedestrian motion features, and further combined with the indoor network matching algorithm for acquiring accurate location and floor observations. In the multi-source fusion procedure, an error ellipse-enhanced unscented Kalman filter is developed for the intelligent combination of a trajectory estimator, human motion constraints, and the extracted pedestrian network. Comprehensive experiments indicate that the presented ML-ISNM achieves autonomous and accurate multi-floor positioning performance in complex and large-scale urban buildings. The final evaluated average localization error was lower than 1.13 m without the assistance of wireless facilities or a navigation database. Full article

(This article belongs to the Special Issue Concurrent Positioning, Mapping and Perception of Multi-Source Data Fusion for Smart Applications II)

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24 pages, 7467 KiB

Open AccessArticle

Multi-Level Fusion Indoor Positioning Technology Considering Credible Evaluation Analysis

by Lu Huang, Baoguo Yu, Shitong Du, Jun Li, Haonan Jia and Jingxue Bi

Remote Sens. 2023, 15(2), 353; https://doi.org/10.3390/rs15020353 - 06 Jan 2023

Cited by 4 | Viewed by 1589

Abstract

Aiming at the problems of the low robustness and poor reliability of a single positioning source in complex indoor environments, a multi-level fusion indoor positioning technology considering credible evaluation is proposed. A multi-dimensional electromagnetic atlas including pseudolites (PL), Wi-Fi and a geomagnetic field is constructed, and the unsupervised learning model is used to sample in the latent space to achieve a feature-level fusion positioning. A location credibility evaluation method is designed to improve the credibility of the positioning system through a multi-dimensional data quality evaluation and heterogeneous information auxiliary constraints. Finally, a large number of experiments were carried out in the laboratory environment, and, finally, about 90% of the positioning error was better than 1 m, and the average positioning error was 0.56 m. Compared with several relatively advanced positioning methods (Inter-satellite CPDM/Epoch-CPDS/Z-KPI) at present, the average positioning accuracy is improved by about 56%, 83.5% and 82.9%, respectively, which verifies the effectiveness of the algorithm. To verify the effect of the proposed method in a practical application environment, the proposed positioning system is deployed in the 2022 Winter Olympics venues. The results show that the proposed method has a significant improvement in the positioning accuracy and continuity. Full article

(This article belongs to the Special Issue Concurrent Positioning, Mapping and Perception of Multi-Source Data Fusion for Smart Applications II)

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22 pages, 5245 KiB

Open AccessArticle

Map-Assisted 3D Indoor Localization Using Crowd-Sensing-Based Trajectory Data and Error Ellipse-Enhanced Fusion

by Qiao Wan, Yue Yu, Ruizhi Chen and Liang Chen

Remote Sens. 2022, 14(18), 4636; https://doi.org/10.3390/rs14184636 - 16 Sep 2022

Cited by 2 | Viewed by 1462

Abstract

Crowd-sensing-based localization is regarded as an effective method for providing indoor location-based services in large-scale urban areas. The performance of the crowd-sensing approach is subject to the poor accuracy of collected daily-life trajectories and the efficient combination of different location sources and indoor maps. This paper proposes a robust map-assisted 3D Indoor localization framework using crowd-sensing-based trajectory data and error ellipse-enhanced fusion (ML-CTEF). In the off-line phase, novel inertial odometry which contains the combination of 1D-convolutional neural networks (1D-CNN) and Bi-directional Long Short-Term Memory (Bi-LSTM)-based walking speed estimator is proposed for accurate crowd-sensing trajectories data pre-processing under different handheld modes. The Bi-LSTM network is further applied for floor identification, and the indoor network matching algorithm is adopted for the generation of fingerprinting database without pain. In the online phase, an error ellipse-assisted particle filter is proposed for the intelligent integration of inertial odometry, crowdsourced Wi-Fi fingerprinting, and indoor map information. The experimental results prove that the proposed ML-CTEF realizes autonomous and precise 3D indoor localization performance under complex and large-scale indoor environments; the estimated average positioning error is within 1.01 m in a multi-floor contained indoor building. Full article

(This article belongs to the Special Issue Concurrent Positioning, Mapping and Perception of Multi-Source Data Fusion for Smart Applications II)

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Concurrent Positioning, Mapping and Perception of Multi-Source Data Fusion for Smart Applications II

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