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Sustainable Artificial Intelligence in Mobile Environment Sensing and Localization
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Sustainable Artificial Intelligence in Mobile Environment Sensing and Localization

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainability in Geographic Science".

Deadline for manuscript submissions: 31 October 2024 | Viewed by 1446

Special Issue Editors

Dr. Yue Yu

E-Mail Website
Guest Editor
Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong 999077, China
Interests: geospatial data analysis; LiDAR cloud data processing; urban informatics
Special Issues, Collections and Topics in MDPI journals
Dr. Lei Wang

E-Mail Website
Guest Editor
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Interests: GNSS positioning; GNSS remote sensing; atmosphere modeling; LEO navigation augmentation
Special Issues, Collections and Topics in MDPI journals
Dr. Jianxin Jia

E-Mail Website
Guest Editor
Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Vuorimiehentie 5, FI-02150 Espoo, Finland
Interests: Artificial Intelligence; Hyperspectral remote sensing; hyperspectral LiDAR
Special Issues, Collections and Topics in MDPI journals
Dr. Zhewei Liu

E-Mail Website
Guest Editor
Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX 77843, USA
Interests: GeoAI; spatial big data analytics; human mobility
Dr. Zuoya Liu

E-Mail Website
Guest Editor
Finish Geospatial Research Institute in National Land Survey of Finland, Vuorimiehentie 5, 02150 Espoo, Finland
Interests: positioning and navigation technologies; multi-sensor fusion; robotics; LiDAR scanning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Currently, with the growing requirements of mobile environment sensing, navigation, and localization, how to intelligently process and integrate multi-source data provided by different sensing and positioning sources is critical for existing smart city applications and fundamental infrastructures, e.g., the unmanned vehicle/aircraft, urban navigation, human mobility analysis. In addition, the sustainable development of artificial intelligence (AI) provides an advanced choice for mobile sensing and localization purposes compared with traditional filter-based or graph-based structures, by extracting and training motion-related features from massive daily-life data for directly model prediction. At this stage, multi-source and multi-model assisted sensing and localization data fusion technology is proven to be an effective approach for the enhancement of location acquirement and sustainable environment sensing for unmanned systems and smart devices, which can provide a more robust result compared with single-source data.

Apart from improving the sustainable performance of urban navigation and positioning in signal-challenging scenes, environment sensing and mapping—i.e., indoors, tunnels, underground, etc. Advanced fusion technologies sustainability on various MEMS sensors and other mobile sensing devices—i.e., MEMS gyroscope, Fiber Optic Gyroscope, ranging and measurement modules, etc.—are key to supporting sensing and localization in complex urban environments. Besides, data-driven and AI-driven based mobile sensing and localization framework is also prospective solution. This Special Issue aims to provide a platform for researchers to publish innovative work on advanced mobile sensing and localization technology sustainability under challenging or signal-denied urban environments using either the AI-driven or filter and graph-based approaches.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

  • Sustainable AI model for mobile sensing;
  • Sustainable AI model for indoor and outdoor localization;
  • Error modelling of different location sources
  • Uncertainty prediction of geospatial data
  • LiDAR/Visual/Inertial/ GNSS Integrated SLAM;
  • Multi-sensor integration and fusion;
  • Sustainable Data-driven based positioning and navigation structure;
  • Sensors Error modelling and elimination;
  • Sustainable AI-driven based fusion and positioning model;
  • Indoor and Outdoor Scene recognition and Seamless Positioning;
  • Tightly-coupled/Loosely-coupled navigation and positioning;
  • Cooperative navigation and positioning;
  • Sustainable Crowdsourced Data Acquisition and Fusion.

We look forward to receiving your contributions.

Dr. Yue Yu
Dr. Lei Wang
Dr. Jianxin Jia
Dr. Zhewei Liu
Dr. Zuoya Liu
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. Sustainability 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 2400 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

  • uncertainty prediction
  • multi-source integration
  • LiDAR/visual SLAM
  • sustainable data-driven
  • sustainable AI-driven
  • cooperative navigation
  • inertial navigation system
  • mobile sensing and localization
  • sustainable environment sensing
  • autonomous driving
  • sustainable crowdsourced data acquisition

Published Papers (1 paper)

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Research

21 pages, 2367 KiB  
Article
A Tight Coupling Algorithm for Strapdown Inertial Navigation System (SINS)/Global Positioning System (GPS) Adaptive Integrated Navigation Based on Variational Bayesian
by Jiaxin Liu, Ke Di, Hui Peng and Yu Liu
Sustainability 2023, 15(16), 12477; https://doi.org/10.3390/su151612477 - 16 Aug 2023
Viewed by 793
Abstract
Multi-source nonlinear noise exists in the process of multi-source navigation information fusion in long-endurance positioning systems in complex environments. In such engineering applications, the classical Kalman filter (KF) and the extended Kalman filter (EKF) have the phenomena of noise instability and parameter drift, [...] Read more.
Multi-source nonlinear noise exists in the process of multi-source navigation information fusion in long-endurance positioning systems in complex environments. In such engineering applications, the classical Kalman filter (KF) and the extended Kalman filter (EKF) have the phenomena of noise instability and parameter drift, which lead to the divergence of filtering results and reductions in accuracy over long periods of time. Aiming at the above problems, this paper proposes a fusion algorithm of the variational Bayesian (VB) and the cubature Kalman filter (CKF). Firstly, the system is modeled through nonlinear filtering, and the CKF error equation is established by taking the position difference and velocity difference between SINS and GPS as observation variables. Then, to address the problem of poor self-adaptation of the CKF algorithm, the variational Bayesian adaptive estimation method is introduced into the CKF algorithm, and a measurement noise variance estimation model is introduced to the process of time and measurement updates of the CKF algorithm to finally obtain the adaptive VB–CKF algorithm. The simulation results from the experimental platform show that the proposed fusion algorithm improves the combined SINS/GPS system by about 30% in terms of attitude angle accuracy and reduces speed and position estimation errors (RMSE) by about 45%. At the same time, comprehensive experiments on multiple types of sites show that compared with the CKF algorithm, the VB–CKF algorithm improves the positioning accuracy by 10% when the GPS signal is stable and improves the accuracy by about 38% when the GPS measurement noise changes dramatically in complex terrain, which effectively suppresses the accuracy divergence of the CKF algorithm and has high value for engineering applications. Full article
(This article belongs to the Special Issue Sustainable Artificial Intelligence in Mobile Environment Sensing and Localization)
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