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Applied Sciences
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Navigation and Object Recognition with 3D Point Clouds
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Navigation and Object Recognition with 3D Point Clouds

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

Deadline for manuscript submissions: closed (20 June 2024) | Viewed by 1866

Special Issue Editors

Prof. Dr. Linyuan Xia

E-Mail Website
Guest Editor
School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
Interests: geodesy; surveying and mapping engineering; cartography and geographic information system
Dr. Ting On Chan

E-Mail Website
Guest Editor
School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
Interests: 3D point cloud; photogrammetry; sensor fusion; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

A 3D point cloud is a 3D coordinate point arranged in a regular grid, which is usually generated by a 3D scanner or photogrammetry software, that can represent a 3D shape or object. It is generally used for visualization, animation, rendering, and mass customization applications.

In the processes of UAV remote sensing image monitoring and automatic driving, high-resolution positioning and the identification of objects are required to obtain more information. Three-dimensional point cloud technology can accurately measure the position and shape of objects in three-dimensional space, which has become a hot topic in recent years. There are five steps: namely, the collection point cloud data, feature extraction, segmentation, classification, and visualization. The processing of 3D data is more complex, so many studies will also use hybrid algorithms combined with this technology. This Special Issue aims to study object positioning and recognition based on 3D point clouds, focusing on applications and not being limited to a particular field. Authors are encouraged to submit relevant research articles or review articles on the above-mentioned topics.

Prof. Dr. Linyuan Xia
Dr. Ting On Chan
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. 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

  • 3D point cloud
  • point cloud data
  • object recognition
  • positioning
  • location
  • high-resolution

Published Papers (2 papers)

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Research

18 pages, 28127 KiB  
Article
Estimating Riparian Vegetation Volume in the River by 3D Point Cloud from UAV Imagery and Alpha Shape
by Eunkyung Jang and Woochul Kang
Appl. Sci. 2024, 14(1), 20; https://doi.org/10.3390/app14010020 - 19 Dec 2023
Viewed by 820
Abstract
This study employs technology that has many different applications, including flood management, flood level control, and identification of vegetation type by patch size. Recent climate change, characterized by severe droughts and floods, intensifies riparian vegetation growth, demanding accurate environmental data. Traditional methods for [...] Read more.
This study employs technology that has many different applications, including flood management, flood level control, and identification of vegetation type by patch size. Recent climate change, characterized by severe droughts and floods, intensifies riparian vegetation growth, demanding accurate environmental data. Traditional methods for analyzing vegetation in rivers involve on-site measurements or estimating the growth phase of the vegetation; however, these methods have limitations. Unmanned aerial vehicles (UAVs) and ground laser scanning, meanwhile, offer cost-effective, versatile solutions. This study uses UAVs to generate 3D riparian vegetation point clouds, employing the alpha shape technique. Performance was evaluated by analyzing the estimated volume results, considering the influence of the alpha radius. Results are most significant with an alpha radius of 0.75. This technology benefits river management by addressing vegetation volume, scale, flood control, and identification of vegetation type. Full article
(This article belongs to the Special Issue Navigation and Object Recognition with 3D Point Clouds)
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13 pages, 3592 KiB  
Article
Estimation of Earth’s Central Angle Threshold and Measurement Model Construction Method for Pose and Attitude Solution Based on Aircraft Scene Matching
by Haiqiao Liu, Zichao Gong, Taixin Liu and Jing Dong
Appl. Sci. 2023, 13(18), 10051; https://doi.org/10.3390/app131810051 - 6 Sep 2023
Viewed by 718
Abstract
To address the challenge of solving aircraft’s visual navigation results using scene matching, this paper introduces the spherical EPnP positioning posture-solving method, which incorporates the threshold value for the central angle and the construction of a measurement model. The detailed steps are as [...] Read more.
To address the challenge of solving aircraft’s visual navigation results using scene matching, this paper introduces the spherical EPnP positioning posture-solving method, which incorporates the threshold value for the central angle and the construction of a measurement model. The detailed steps are as follows: Firstly, the positioning coordinate model of the Earth’s surface is constructed to ensure the expression of the three-dimensional coordinates of the Earth’s surface. The positioning is then solved by employing the EPnP positioning posture-solving algorithm on the constructed data model. Secondly, by comparing and analyzing the positioning posture values of approximate plane coordinates, the critical value is determined, which serves as a reference for plane calculations. Lastly, a theoretical measurement model for visual height and central angle is constructed, taking into account the decided central angle threshold value. The simulation experiment demonstrates that using spherical coordinates as input results in an average positioning precision that is 16.42 percent higher compared to using plane coordinates as input. When the central angle is less than 0.5 degrees and the surface area is smaller than 558502 square meters, the positioning precision of plane coordinates is comparable to that of spherical coordinates. In such instances, the sphere can be approximated as flat. The findings of this study provide important theoretical guidance for further research on scene-matching positioning posture solving. These results hold significant implications for both theoretical research and engineering applications. Full article
(This article belongs to the Special Issue Navigation and Object Recognition with 3D Point Clouds)
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