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Environmental Monitoring Using UAV and Mobile Mapping Systems

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

Deadline for manuscript submissions: 15 November 2024 | Viewed by 1284

Special Issue Editor


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Guest Editor
Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA
Interests: photogrammetry; laser scanning; mobile mapping systems; system calibration; computer vision; unmanned aerial mapping systems; multisensor/multiplatform data integration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the increase in global population, decrease in available resources, and growing interest in protecting our environment, we have an unprecedent need to develop accurate, affordable tools for the digital documentation and inventory of our environment. Mobile mapping systems equipped with passive and active sensing modalities have been proven as accurate modalities for the accurate documentation of our surroundings. Advances in direct georeferencing technologies (i.e., integrated global navigation satellite systems and inertial navigation systems—GNSS/INS), passive sensing technologies operating in different portions of the electromagnetic spectrum (e.g., RGB, multi-spectral, and hyperspectral cameras), active ranging systems (e.g., linear and single-photon light detection and ranging—LiDAR), and platforms (e.g., crewed and uncrewed aerial/ground vehicles) are providing unprecedent opportunities for the accurate, up-to-date, and affordable mapping of our environment. This Special Issue is seeking contributions that deal with different aspects of using mobile mapping technologies, in general, and uncrewed aerial vehicles, in particular, for environmental monitoring applications. Papers related to the topics below, as well as others, are welcomed:

  • Remote sensing using un-crewed aerial vehicles (UAVs);
  • System calibration and control-free mapping applications;
  • GNSS/INS-based georeferencing of remote sensing systems;
  • Visual SLAM for GNSS-denied/challenging environments;
  • LiDAR SLAM for GNSS-denied/challenging environments;
  • Hybrid (visual/LiDAR) SLAM for GNSS-denied/challenging environments;
  • Learning and geometric strategies for processing passive and active remote sensing data;
  • Fusion of passive and active remote sensing data;
  • Quantitative change detection using passive and active remote sensing data;
  • Quality control of remote sensing data and products;
  • Geiger mode and single photo LiDAR systems for scalable mapping of larger areas;
  • Fine-resolution forest inventory;
  • Multi-spectral and hyperspectral remote sensing of agricultural fields;
  • Management of coastal regions using mobile mapping technologies;
  • Remote sensing data for digital twin generation.

Prof. Dr. Ayman F. Habib
Guest Editor

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.

Published Papers (1 paper)

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Research

26 pages, 10606 KiB  
Article
Correlative Scan Matching Position Estimation Method by Fusing Visual and Radar Line Features
by Yang Li, Xiwei Cui, Yanping Wang and Jinping Sun
Remote Sens. 2024, 16(1), 114; https://doi.org/10.3390/rs16010114 - 27 Dec 2023
Viewed by 844
Abstract
Millimeter-wave radar and optical cameras are one of the primary sensing combinations for autonomous platforms such as self-driving vehicles and disaster monitoring robots. The millimeter-wave radar odometry can perform self-pose estimation and environmental mapping. However, cumulative errors can arise during extended measurement periods. [...] Read more.
Millimeter-wave radar and optical cameras are one of the primary sensing combinations for autonomous platforms such as self-driving vehicles and disaster monitoring robots. The millimeter-wave radar odometry can perform self-pose estimation and environmental mapping. However, cumulative errors can arise during extended measurement periods. In particular scenes where loop closure conditions are absent and visual geometric features are discontinuous, existing loop detection methods based on back-end optimization face challenges. To address this issue, this study introduces a correlative scan matching (CSM) pose estimation method that integrates visual and radar line features (VRL-SLAM). By making use of the pose output and the occupied grid map generated by the front end of the millimeter-wave radar’s simultaneous localization and mapping (SLAM), it compensates for accumulated errors by matching discontinuous visual line features and radar line features. Firstly, a pose estimation framework that integrates visual and radar line features was proposed to reduce the accumulated errors generated by the odometer. Secondly, an adaptive Hough transform line detection method (A-Hough) based on the projection of the prior radar grid map was introduced, eliminating interference from non-matching lines, enhancing the accuracy of line feature matching, and establishing a collection of visual line features. Furthermore, a Gaussian mixture model clustering method based on radar cross-section (RCS) was proposed, reducing the impact of radar clutter points online feature matching. Lastly, actual data from two scenes were collected to compare the algorithm proposed in this study with the CSM algorithm and RI-SLAM.. The results demonstrated a reduction in long-term accumulated errors, verifying the effectiveness of the method. Full article
(This article belongs to the Special Issue Environmental Monitoring Using UAV and Mobile Mapping Systems)
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