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Applied Sciences
Special Issues
Lidar Technology and Application
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Lidar Technology and Application

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Optics and Lasers".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 1919

Special Issue Editor

Prof. Dr. Suhui Yang

E-Mail Website
Guest Editor
School of Optics and Photonics, Beijing Institute of Technology, Beijing, China
Interests: solar pumped lasers; laser wireless energy transmission

Special Issue Information

Dear Colleagues,

LiDAR systems are widely used in surveying, mapping, transportation, industry, agriculture, archaeology, ocean exploration, atmospheric remote sensing and other fields. The purpose of this Special Issue is to provide a platform for scholars to share the latest achievements in this field. Researchers are encouraged to submit papers on LiDAR-related theory, design, experiments, applications, signal processing, system modeling, system composition, technology, light sources, optical systems, optical signal detection, and numerical simulations. Topics of interest for this Special Issue include, but are not limited to: wind LiDAR; CO2 LiDAR; underwater LiDAR; LiDAR mapping, dial LiDAR, methane detection LiDAR, forestry LiDAR; 3D LiDAR; and autonomous driving LiDAR. Reviews of LiDAR-related developments of systems and technologies are also welcome.

Prof. Dr. Suhui Yang
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. Applied Sciences 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

  • wind lidar
  • CO2 lidar
  • under water lidar
  • lidar mapping
  • dial lidar
  • methane detection lidar
  • forestry lidar

Published Papers (1 paper)

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Research

17 pages, 4051 KiB  
Article
Fusion Segmentation Network Guided by Adaptive Sampling Radius and Channel Attention Mechanism Module for MLS Point Clouds
by Peng Cheng, Ming Guo, Haibo Wang, Zexin Fu, Dengke Li and Xian Ren
Appl. Sci. 2023, 13(1), 281; https://doi.org/10.3390/app13010281 - 26 Dec 2022
Cited by 3 | Viewed by 1386
Abstract
Road high-precision mobile LiDAR measurement point clouds are the digital infrastructures for high-precision maps, autonomous driving, digital twins, etc. High-precision automated semantic segmentation of road point clouds is a crucial research direction. Aiming at the problem of low semantic segmentation accuracy of existing [...] Read more.
Road high-precision mobile LiDAR measurement point clouds are the digital infrastructures for high-precision maps, autonomous driving, digital twins, etc. High-precision automated semantic segmentation of road point clouds is a crucial research direction. Aiming at the problem of low semantic segmentation accuracy of existing deep learning networks for inhomogeneous sparse point clouds of mobile LiDAR system measurements (MLS), a deep learning method that adaptively adjusts the sampling radius of region groups according to the point clouds density is proposed. We construct a deep learning road point clouds dataset based on a self-developed mobile LiDAR system to train and test road point clouds semantic segmentation. The overall accuracy of the method for road point clouds segmentation is 98.08%, with an overall mIOU of 0.73 and mIOUs of 0.99, 0.983, 0.99, 0.66, and 0.51 for roads, guardrails, signs, streetlights, and lane lines, respectively. The experimental result shows that the method can achieve more accurate segmentation for inhomogeneous sparse road point clouds of mobile LiDAR systems. Compared with the existing methods, the segmentation accuracy is significantly improved. Full article
(This article belongs to the Special Issue Lidar Technology and Application)
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