Special Issue "LiDAR and Time-of-flight Imaging"

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

Deadline for manuscript submissions: 31 March 2019

Special Issue Editors

Guest Editor
Prof. Dr. Santiago Royo Royo

Director of Centre for Sensors, Instruments and Systems Development (CD6), Universitat Politècnica de Catalunya, Rambla Sant Nebridi 10, E08222 Terrassa, Spain
Website | E-Mail
Interests: lidar imaging, active optics, optical metrology, optomechanical engineering
Guest Editor
Dr. Jan-Erik Kallhammer

Director of Visual Enhancement and Cognitive Systems, Veoneer, Inc. SE-103 02 Stockholm, Sweden
Website | E-Mail
Interests: lidar- and gated imaging, imaging performance in inclement weather, functional specification, cognitive system

Special Issue Information

Dear Colleagues,

Time-of-flight and lidar imaging are currently one the main drivers of the applied development in optomechanics and electronics. There is a compelling need to develop robust and cost-effective lidar sensors for the autonomous vehicle industry, and in particular for automotives. This has resulted in a number of different radiometric modelling approaches, and in intense activity in the development of novel components, including sources, detectors, and optics. Further, several sensing strategies are being proposed beyond the classical pulsed and modulated approaches, which may involve sophisticated components such as optical phased arrays or MEMS scanners to solve a problem that pushes the state of the art of current technology. Advances in lidar, however, also need progress in the behavior of lidar imaging units in inclment weather, or on the software side, as in strategies for the management of dense point-clouds in real time, or in miniaturization for mobile phone applications. Progress beyond the state of the art in such a number of different fields of applied science activities is required to bring lidar imagers closer to become the next step in optical imaging and to change our perception of the world.

Prof. Dr. Santiago Royo Royo
Dr. Jan-Erik Kallhammer
Guest Editors

Manuscript Submission Information

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Keywords

  • Lidar imaging
  • Sources and detectors for lidar imagers
  • Novel measurement schemes
  • Point cloud sensing and management

Published Papers (6 papers)

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Research

Open AccessArticle The Influence of the Cartographic Transformation of TLS Data on the Quality of the Automatic Registration
Appl. Sci. 2019, 9(3), 509; https://doi.org/10.3390/app9030509
Received: 15 November 2018 / Revised: 19 January 2019 / Accepted: 29 January 2019 / Published: 1 February 2019
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Abstract
This paper discusses the issue of the influence of cartographic Terrestrial Laser Scanning (TLS) data conversion into feature-based automatic registration. Automatic registration of data is a multi-stage process, it is based on original software tools and consists of: (1) Conversion of data to [...] Read more.
This paper discusses the issue of the influence of cartographic Terrestrial Laser Scanning (TLS) data conversion into feature-based automatic registration. Automatic registration of data is a multi-stage process, it is based on original software tools and consists of: (1) Conversion of data to the raster form, (2) register of TLS data in pairs in all possible combinations using the SURF (Speeded Up Robust Features) and FAST (Features from Accelerated Segment Test) algorithms, (3) the quality analysis of relative orientation of processed pairs, and (4) the final bundle adjustment. The following two problems, related to the influence of the spherical image, the orthoimage and the Mercator representation of the point cloud, are discussed: The correctness of the automatic tie points detection and distribution and the influence of the TLS position on the completeness of the registration process and the quality assessment. The majority of popular software applications use manually or semi-automatically determined corresponding points. However, the authors propose an original software tool to address the first issue, which automatically detects and matches corresponding points on each TLS raster representation, utilizing different algorithms (SURF and FAST). To address the second task, the authors present a series of analyses: The time of detection of characteristic points, the percentage of incorrectly detected points and adjusted characteristic points, the number of detected control and check points, the orientation accuracy of control and check points, and the distribution of control and check points. Selection of an appropriate method for the TLS point cloud conversion to the raster form and selection of an appropriate algorithm, considerably influence the completeness of the entire process, and the accuracy of data orientation. The results of the performed experiments show that fully automatic registration of the TLS point clouds in the raster forms is possible; however, it is not possible to propose one, universal form of the point cloud, because a priori knowledge concerning the scanner positions is required. If scanner stations are located close to one another in raster images or in spherical images, Mercator projections are recommended. In the case where fragments of the surface are measured under different angles from different distances and heights of the TLS, orthoimages are suggested. Full article
(This article belongs to the Special Issue LiDAR and Time-of-flight Imaging)
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Open AccessArticle An Improved Skewness Balancing Filtering Algorithm Based on Thin Plate Spline Interpolation
Appl. Sci. 2019, 9(1), 203; https://doi.org/10.3390/app9010203
Received: 7 November 2018 / Revised: 19 December 2018 / Accepted: 3 January 2019 / Published: 8 January 2019
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Abstract
Most filtering algorithms suffer from complex parameter settings or threshold adjusting. To solve this problem, this paper proposes an improved skewness balancing filtering algorithm based on thin plate spline (TPS) interpolation. The proposed algorithm filters the nonground points in an iterative manner. A [...] Read more.
Most filtering algorithms suffer from complex parameter settings or threshold adjusting. To solve this problem, this paper proposes an improved skewness balancing filtering algorithm based on thin plate spline (TPS) interpolation. The proposed algorithm filters the nonground points in an iterative manner. A reference surface that reflects the fluctuation of the terrain is generated using the TPS interpolation method. Accordingly, the elevation difference from each point to the surface can be calculated. By applying the skewness balancing principle to these elevation differences, nonground points can be removed automatically. To verify the validity and robustness of the proposed method, the datasets provided by the International Society for Photogrammetry and Remote Sensing (ISPRS) were adopted. The experimental results show that this presented method can adapt to complex environments and achieve a higher filtering accuracy than the traditional skewness balancing algorithm. Moreover, in comparison with the other eight filtering methods tested by the ISPRS and four improved filtering methods proposed recently, the proposed method achieved an average total error of 5.39%, which is smaller than that of most of these other methods. Full article
(This article belongs to the Special Issue LiDAR and Time-of-flight Imaging)
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Open AccessArticle Semi-Analytic Monte Carlo Model for Oceanographic Lidar Systems: Lookup Table Method Used for Randomly Choosing Scattering Angles
Appl. Sci. 2019, 9(1), 48; https://doi.org/10.3390/app9010048
Received: 27 November 2018 / Revised: 19 December 2018 / Accepted: 20 December 2018 / Published: 24 December 2018
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Abstract
Monte Carlo (MC) is a significant technique for finding the radiative transfer equation (RTE) solution. Nowadays, the Henyey-Greenstein (HG) scattering phase function (spf) has been widely used in most studies during the core procedure of randomly choosing scattering angles in oceanographic lidar MC [...] Read more.
Monte Carlo (MC) is a significant technique for finding the radiative transfer equation (RTE) solution. Nowadays, the Henyey-Greenstein (HG) scattering phase function (spf) has been widely used in most studies during the core procedure of randomly choosing scattering angles in oceanographic lidar MC simulations. However, the HG phase function does not work well at small or large scattering angles. Other spfs work well, e.g., Fournier-Forand phase function (FF); however, solving the cumulative distribution function (cdf) of the scattering phase function (even if possible) would result in a complicated formula. To avoid the above-mentioned problems, we present a semi-analytic MC radiative transfer model in this paper, which uses the cdf equation to build up a lookup table (LUT) of ψ vs. P Ψ ( ψ ) to determine scattering angles for various spfs (e.g., FF, Petzold measured particle phase function, and so on). Moreover, a lidar geometric model for analytically estimating the probability of photon scatter back to a remote receiver was developed; in particular, inhomogeneous layers are divided into voxels with different optical properties; therefore, it is useful for inhomogeneous water. First, the simulations between the inverse function method for HG cdf and the LUT method for FF cdf were compared. Then, multiple scattering and wind-driven sea surface condition effects were studied. Finally, we compared our simulation results with measurements of airborne lidar. The mean relative errors between simulation and measurements in inhomogeneous water are within 14% for the LUT method and within 22% for the inverse cdf (ICDF) method. The results suggest feasibility and effectiveness of our simulation model. Full article
(This article belongs to the Special Issue LiDAR and Time-of-flight Imaging)
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Open AccessArticle Time Resolution Improvement Using Dual Delay Lines for Field-Programmable-Gate-Array-Based Time-to-Digital Converters with Real-Time Calibration
Appl. Sci. 2019, 9(1), 20; https://doi.org/10.3390/app9010020
Received: 8 December 2018 / Revised: 14 December 2018 / Accepted: 18 December 2018 / Published: 21 December 2018
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Abstract
This paper presents a time-to-digital converter (TDC) based on a field programmable gate array (FPGA) with a tapped delay line (TDL) architecture. This converter employs dual delay lines (DDLs) to enable real-time calibrations, and the proposed DDL-TDC measures the statistical distribution of delays [...] Read more.
This paper presents a time-to-digital converter (TDC) based on a field programmable gate array (FPGA) with a tapped delay line (TDL) architecture. This converter employs dual delay lines (DDLs) to enable real-time calibrations, and the proposed DDL-TDC measures the statistical distribution of delays to permit the calibration of nonuniform delay cells in FPGA-based TDC designs. DDLs are also used to set up alternate calibrations, thus enabling environmental effects to be immediately accounted for. Experimental results revealed that relative to a conventional TDL-TDC, the proposed DDL-TDC reduced the maximum differential nonlinearity by 26% and the integral nonlinearity by 30%. A root-mean-squared value of 32 ps was measured by inputting the constant delay source into the proposed DDL-TDC. The proposed scheme also maintained excellent linearity across a range of temperatures. Full article
(This article belongs to the Special Issue LiDAR and Time-of-flight Imaging)
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Open AccessArticle Development of Image Processing for Crack Detection on Concrete Structures through Terrestrial Laser Scanning Associated with the Octree Structure
Appl. Sci. 2018, 8(12), 2373; https://doi.org/10.3390/app8122373
Received: 18 October 2018 / Revised: 12 November 2018 / Accepted: 21 November 2018 / Published: 23 November 2018
Cited by 1 | PDF Full-text (7612 KB) | HTML Full-text | XML Full-text
Abstract
Terrestrial laser scanning (TLS) provides a rapid remote sensing technique to model 3D objects but can also be used to assess the surface condition of structures. In this study, an effective image processing technique is proposed for crack detection on images extracted from [...] Read more.
Terrestrial laser scanning (TLS) provides a rapid remote sensing technique to model 3D objects but can also be used to assess the surface condition of structures. In this study, an effective image processing technique is proposed for crack detection on images extracted from the octree structure of TLS data. To efficiently utilize TLS for the surface condition assessment of large structures, a process was constructed to compress the original scanned data based on the octree structure. The point cloud data obtained by TLS was converted into voxel data, and further converted into an octree data structure, which significantly reduced the data size but minimized the loss of resolution to detect cracks on the surface. The compressed data was then used to detect cracks on the surface using a combination of image processing algorithms. The crack detection procedure involved the following main steps: (1) classification of an image into three categories (i.e., background, structural joints and sediments, and surface) using K-means clustering according to color similarity, (2) deletion of non-crack parts on the surface using improved subtraction combined with median filtering and K-means clustering results, (3) detection of major crack objects on the surface based on Otsu’s binarization method, and (4) highlighting crack objects by morphological operations. The proposed technique was validated on a spillway wall of a concrete dam structure in South Korea. The scanned data was compressed up to 50% of the original scanned data, while showing good performance in detecting cracks with various shapes. Full article
(This article belongs to the Special Issue LiDAR and Time-of-flight Imaging)
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Open AccessArticle LIDAR Point Cloud Registration for Sensing and Reconstruction of Unstructured Terrain
Appl. Sci. 2018, 8(11), 2318; https://doi.org/10.3390/app8112318
Received: 21 October 2018 / Revised: 14 November 2018 / Accepted: 18 November 2018 / Published: 21 November 2018
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Abstract
When 3D laser scanning (LIDAR) is used for navigation of autonomous vehicles operated on unstructured terrain, it is necessary to register the acquired point cloud and accurately perform point cloud reconstruction of the terrain in time. This paper proposes a novel registration method [...] Read more.
When 3D laser scanning (LIDAR) is used for navigation of autonomous vehicles operated on unstructured terrain, it is necessary to register the acquired point cloud and accurately perform point cloud reconstruction of the terrain in time. This paper proposes a novel registration method to deal with uneven-density and high-noise of unstructured terrain point clouds. It has two steps of operation, namely initial registration and accurate registration. Multisensor data is firstly used for initial registration. An improved Iterative Closest Point (ICP) algorithm is then deployed for accurate registration. This algorithm extracts key points and builds feature descriptors based on the neighborhood normal vector, point cloud density and curvature. An adaptive threshold is introduced to accelerate iterative convergence. Experimental results are given to show that our two-step registration method can effectively solve the uneven-density and high-noise problem in registration of unstructured terrain point clouds, thereby improving the accuracy of terrain point cloud reconstruction. Full article
(This article belongs to the Special Issue LiDAR and Time-of-flight Imaging)
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