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Advances in Mobile LiDAR Point Clouds
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Advances in Mobile LiDAR Point Clouds

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Radar Sensors".

Deadline for manuscript submissions: closed (30 January 2025) | Viewed by 2738

Special Issue Editor

Prof. Dr. Sylvie Daniel

E-Mail Website
Guest Editor
Department of Geomatics Sciences, Université Laval, 1055 Avenue du Séminaire, Quebec City, QC G1V 0A6, Canada
Interests: images and LiDAR& bathymetric point cloud acquisition; image & point cloud processing; 3D modeling & representation; augmented reality; data fusion; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, remarkable strides have been made in the field of mobile LiDAR technology, particularly in the acquisition and processing of point cloud data. Mobile LiDAR systems, mounted on an ever-increasing variety of platforms, have become instrumental in revolutionizing the way we capture and analyze spatial information. Despite significant advancements, several challenges persist in the processing of mobile LiDAR point cloud. The vast quantities of three-dimensional point cloud data with higher densities lead to challenge in storage, data transfer, efficient request of information, and visualization. Additionallu, achieving accurate registration and alignment of point clouds for creating a seamless and accurate representation of the environment persists as a research issue. Mobile LiDAR is susceptible to noise and occlusions, outliers which impact the point cloud and the capacity to reliably recognize object and extract meaningful features. Given the large volumes of point cloud data, such recognition and feature extraction require advanced automation to reduce the requisite effort and advance processing in a timely manner. Machine learning and artificial intelligence techniques, including deep learning, are increasingly used for such purposes. However, these methods run into difficulties when little data are available in the targeted application context, such as in natural environment (e.g., forests, rivers) or change detection, for instance. Mobile LiDAR systems are often used in conjunction with other sensors (e.g., cameras, GPS) to provide comprehensive information. Integrating and synchronizing data from multiple sources can be challenging.

This Special Issue aims to advance our understanding of how these challenges involved in the processing of mobile LiDAR point clouds can be addressed and overcome. We welcome original work on the following topics:

  • data lake for point cloud
  • point cloud accuracy and uncertainty
  • denoising
  • occlusion and completion
  • machine and deep learning
  • surface and 3D reconstruction
  • change detection
  • point cloud simulation
  • point cloud visualization

Prof. Dr. Sylvie Daniel
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. Sensors 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 2600 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

  • data lake for point cloud
  • point cloud accuracy and uncertainty
  • denoising
  • occlusion and completion
  • machine and deep learning
  • surface and 3D reconstruction
  • change detection
  • point cloud simulation
  • point cloud visualization

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Published Papers (2 papers)

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Research

17 pages, 10616 KiB  
Article
Filtering-Assisted Airborne Point Cloud Semantic Segmentation for Transmission Lines
by Wanjing Yan, Weifeng Ma, Xiaodong Wu, Chong Wang, Jianpeng Zhang and Yuncheng Deng
Sensors 2024, 24(21), 7028; https://doi.org/10.3390/s24217028 - 31 Oct 2024
Viewed by 878
Abstract
Point cloud semantic segmentation is crucial for identifying and analyzing transmission lines. Due to the number of point clouds being huge, complex scenes, and unbalanced sample proportion, the mainstream machine learning methods of point cloud segmentation cannot provide high efficiency and accuracy when [...] Read more.
Point cloud semantic segmentation is crucial for identifying and analyzing transmission lines. Due to the number of point clouds being huge, complex scenes, and unbalanced sample proportion, the mainstream machine learning methods of point cloud segmentation cannot provide high efficiency and accuracy when extending to transmission line scenes. This paper proposes a filter-assisted airborne point cloud semantic segmentation for transmission lines. First, a large number of ground point clouds is identified by introducing the well-developed cloth simulation filter to alleviate the impact of the imbalance of the target object proportion on the classifier’s performance. The multi-dimensional features are then defined, and the classification model is trained to achieve the multi-element semantic segmentation of the transmission line scene. The experimental results and analysis indicate that the proposed filter-assisted algorithm can significantly improve the semantic segmentation performance of the transmission line point cloud, enhancing both the point cloud segmentation efficiency and accuracy by more than 25.46% and 3.15%, respectively. The filter-assisted point cloud semantic segmentation method reduces the volume of sample data, the number of sample classes, and the sample imbalance index in power line scenarios to a certain extent, thereby improving the classification accuracy of classifiers and reducing time consumption. This research holds significant theoretical reference value and engineering application potential for scene reconstruction and intelligent understanding of airborne laser point cloud transmission lines. Full article
(This article belongs to the Special Issue Advances in Mobile LiDAR Point Clouds)
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15 pages, 4257 KiB  
Article
The Improvement of Density Peaks Clustering Algorithm and Its Application to Point Cloud Segmentation of LiDAR
by Zheng Wang, Xintong Fang, Yandan Jiang, Haifeng Ji, Baoliang Wang and Zhiyao Huang
Sensors 2024, 24(17), 5693; https://doi.org/10.3390/s24175693 - 1 Sep 2024
Cited by 1 | Viewed by 1260
Abstract
This work focuses on the improvement of the density peaks clustering (DPC) algorithm and its application to point cloud segmentation in LiDAR. The improvement of DPC focuses on avoiding the manual determination of the cut-off distance and the manual selection of cluster centers. [...] Read more.
This work focuses on the improvement of the density peaks clustering (DPC) algorithm and its application to point cloud segmentation in LiDAR. The improvement of DPC focuses on avoiding the manual determination of the cut-off distance and the manual selection of cluster centers. And the clustering process of the improved DPC is automatic without manual intervention. The cut-off distance is avoided by forming a voxel structure and using the number of points in the voxel as the local density of the voxel. The automatic selection of cluster centers is realized by selecting the voxels whose gamma values are greater than the gamma value of the inflection point of the fitted γ curve as cluster centers. Finally, a new merging strategy is introduced to overcome the over-segmentation problem and obtain the final clustering result. To verify the effectiveness of the improved DPC, experiments on point cloud segmentation of LiDAR under different scenes were conducted. The basic DPC, K-means, and DBSCAN were introduced for comparison. The experimental results showed that the improved DPC is effective and its application to point cloud segmentation of LiDAR is successful. Compared with the basic DPC, K-means, the improved DPC has better clustering accuracy. And, compared with DBSCAN, the improved DPC has comparable or slightly better clustering accuracy without nontrivial parameters. Full article
(This article belongs to the Special Issue Advances in Mobile LiDAR Point Clouds)
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