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Special Issue "Terrestrial Laser Scanning"

A special issue of Sensors (ISSN 1424-8220).

Deadline for manuscript submissions: 31 October 2018

Special Issue Editors

Guest Editor
Prof. Dr. Joan R. Rosell Polo

Research Group in AgroICT & Precision Agriculture, University of Lleida – Agrotecnio Center, Catalonia (Spain)
Website | E-Mail
Phone: +34 973 702861
Interests: precision agriculture; remote sensing in agriculture; robotics and automation; sensors; energy
Guest Editor
Dr. Eduard Gregorio López

Research Group in AgroICT & Precision Agriculture, University of Lleida – Agrotecnio Center, Catalonia (Spain)
Website | E-Mail
Interests: sensors; robotics and automation; precision agriculture; LiDAR; energy
Guest Editor
Dr. Jordi Llorens Calveras

Research Group in AgroICT & Precision Agriculture, University of Lleida – Agrotecnio Center, Catalonia (Spain)
Website | E-Mail
Interests: precision agriculture; machinery; sensors; open hardware and software

Special Issue Information

Dear Colleagues,

Since their availability a few decades ago, the applications of LiDAR sensors in 3D scanning have been growing steadily and have been extended to an increasing number of human activity areas and research fields. Specifically, an important effort has been devoted to the characterization of terrestrial targets, both through LiDAR sensors carried by aircrafts and spacecrafts or by terrestrial vehicles. The decrease in size and cost of many LiDAR sensors has led to their popularization among researchers and specialized companies. Simultaneously, the emerging of autonomous vehicles research and development by many car making companies has driven even more the search for more advanced, miniaturized and cheaper LiDAR systems.

The aim of this Special Issue is to bring together innovative developments and applications of Terrestrial Laser Scanning, TLS, understood in a broad sense, i.e. involving the use of Laser-based systems (such as 3D and 2D LiDAR, Flash LiDAR and RGB-D/Depth cameras, etc.) for the measurement and characterization of terrestrial targets. Laser-based systems can be either statically placed on the ground or in motion after being mounted on a moving terrestrial vehicle (mobile terrestrial laser scanners, MTLS). Papers addressing new insights in the development, application and benefits of TLS methods and technology are welcome. Articles may include, but are not limited to, the following topics:

  • New developments of LiDAR and RGB-D sensors and TLS Systems
  • Integration of TLS with other sensors (sensor fusion)
  • Applications of multiple-returns TLS
  • Mobile terrestrial Laser scanner applications (i.e., man-carried and ground-based vehicle-carried)
  • TLS data processing
  • Point cloud creation and processing
  • New applications of LiDAR for the characterization of terrestrial targets
  • Uses and applications from autonomous/unmanned vehicles
  • Agriculture, forestry and geosciences applications
  • Architecture and industrial applications
  • Cultural heritage and archaeological applications

Prof. Dr. Joan R. Rosell Polo
Dr. Eduard Gregorio López
Dr. Jordi Llorens Calveras
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 papers will be 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 monthly 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 1800 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

  • LiDAR
  • Laser Scanning
  • 3D modelling
  • Point cloud
  • Multiple-returns TLS
  • RGB-D sensors
  • SLAM methods
  • Data processing
  • Mobile terrestrial laser scanners

Published Papers (3 papers)

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Research

Open AccessArticle On the Sensitivity of the Parameters of the Intensity-Based Stochastic Model for Terrestrial Laser Scanner. Case Study: B-Spline Approximation
Sensors 2018, 18(9), 2964; https://doi.org/10.3390/s18092964
Received: 12 July 2018 / Revised: 28 August 2018 / Accepted: 3 September 2018 / Published: 5 September 2018
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Abstract
For a trustworthy least-squares (LS) solution, a good description of the stochastic properties of the measurements is indispensable. For a terrestrial laser scanner (TLS), the range variance can be described by a power law function with respect to the intensity of the reflected
[...] Read more.
For a trustworthy least-squares (LS) solution, a good description of the stochastic properties of the measurements is indispensable. For a terrestrial laser scanner (TLS), the range variance can be described by a power law function with respect to the intensity of the reflected signal. The power and scaling factors depend on the laser scanner under consideration, and could be accurately determined by means of calibrations in 1d mode or residual analysis of LS adjustment. However, such procedures complicate significantly the use of empirical intensity models (IM). The extent to which a point-wise weighting is suitable when the derived variance covariance matrix (VCM) is further used in a LS adjustment remains moreover questionable. Thanks to closed loop simulations, where both the true geometry and stochastic model are under control, we investigate how variations of the parameters of the IM affect the results of a LS adjustment. As a case study, we consider the determination of the Cartesian coordinates of the control points (CP) from a B-splines curve. We show that a constant variance can be assessed to all the points of an object having homogeneous properties, without affecting the a posteriori variance factor or the loss of efficiency of the LS solution. The results from a real case scenario highlight that the conclusions of the simulations stay valid even for more challenging geometries. A procedure to determine the range variance is proposed to simplify the computation of the VCM. Full article
(This article belongs to the Special Issue Terrestrial Laser Scanning)
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Open AccessArticle Robust Segmentation of Planar and Linear Features of Terrestrial Laser Scanner Point Clouds Acquired from Construction Sites
Sensors 2018, 18(3), 819; https://doi.org/10.3390/s18030819
Received: 1 December 2017 / Revised: 10 February 2018 / Accepted: 5 March 2018 / Published: 8 March 2018
Cited by 1 | PDF Full-text (11071 KB) | HTML Full-text | XML Full-text
Abstract
Automated segmentation of planar and linear features of point clouds acquired from construction sites is essential for the automatic extraction of building construction elements such as columns, beams and slabs. However, many planar and linear segmentation methods use scene-dependent similarity thresholds that may
[...] Read more.
Automated segmentation of planar and linear features of point clouds acquired from construction sites is essential for the automatic extraction of building construction elements such as columns, beams and slabs. However, many planar and linear segmentation methods use scene-dependent similarity thresholds that may not provide generalizable solutions for all environments. In addition, outliers exist in construction site point clouds due to data artefacts caused by moving objects, occlusions and dust. To address these concerns, a novel method for robust classification and segmentation of planar and linear features is proposed. First, coplanar and collinear points are classified through a robust principal components analysis procedure. The classified points are then grouped using a new robust clustering method, the robust complete linkage method. A robust method is also proposed to extract the points of flat-slab floors and/or ceilings independent of the aforementioned stages to improve computational efficiency. The applicability of the proposed method is evaluated in eight datasets acquired from a complex laboratory environment and two construction sites at the University of Calgary. The precision, recall, and accuracy of the segmentation at both construction sites were 96.8%, 97.7% and 95%, respectively. These results demonstrate the suitability of the proposed method for robust segmentation of planar and linear features of contaminated datasets, such as those collected from construction sites. Full article
(This article belongs to the Special Issue Terrestrial Laser Scanning)
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Open AccessArticle Vertical Optical Scanning with Panoramic Vision for Tree Trunk Reconstruction
Sensors 2017, 17(12), 2791; https://doi.org/10.3390/s17122791
Received: 2 November 2017 / Revised: 29 November 2017 / Accepted: 30 November 2017 / Published: 2 December 2017
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Abstract
This paper presents a practical application of a technique that uses a vertical optical flow with a fisheye camera to generate dense point clouds from a single planimetric station. Accurate data can be extracted to enable the measurement of tree trunks or branches.
[...] Read more.
This paper presents a practical application of a technique that uses a vertical optical flow with a fisheye camera to generate dense point clouds from a single planimetric station. Accurate data can be extracted to enable the measurement of tree trunks or branches. The images that are collected with this technique can be oriented in photogrammetric software (using fisheye models) and used to generate dense point clouds, provided that some constraints on the camera positions are adopted. A set of images was captured in a forest plot in the experiments. Weighted geometric constraints were imposed in the photogrammetric software to calculate the image orientation, perform dense image matching, and accurately generate a 3D point cloud. The tree trunks in the scenes were reconstructed and mapped in a local reference system. The accuracy assessment was based on differences between measured and estimated trunk diameters at different heights. Trunk sections from an image-based point cloud were also compared to the corresponding sections that were extracted from a dense terrestrial laser scanning (TLS) point cloud. Cylindrical fitting of the trunk sections allowed the assessment of the accuracies of the trunk geometric shapes in both clouds. The average difference between the cylinders that were fitted to the photogrammetric cloud and those to the TLS cloud was less than 1 cm, which indicates the potential of the proposed technique. The point densities that were obtained with vertical optical scanning were 1/3 less than those that were obtained with TLS. However, the point density can be improved by using higher resolution cameras. Full article
(This article belongs to the Special Issue Terrestrial Laser Scanning)
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