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Lidar Remote Sensing in 3D Object Modelling

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 23519

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Dr. Linh TRUONG-HONG

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Department of Geoscience and Remote Sensing, Delft University of Technology, Delft, The Netherlands
Interests: point cloud; LiDAR; laser scanning; feature extraction; classification; segmentation; geometric modeling; 3D reconstruction; infrastructure inspection
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Special Issue Information

Dear Colleagues,

Various types of laser scanning sensors have been available in the market, which are offering many data acquisition platforms. Objects can be captured at different levels of details, implying plenty of rich information around the objects available to be captured. Although a lot of algorithms and frameworks have been well developed to process laser scanning data in an automated manner, robust and efficient methods for extracting and modeling three-dimensional (3D) objects in high accuracy are still in high demand and more challenging.

Prospective authors are cordially invited to contribute to this Special issue by submitting the latest, original research in Lidar Remote Sensing in 3D Object Modeling for structural and environmental engineering. Original and innovative studies may cover but not be limited to the following topics:

New methods for extracting objects’ features and objects from point clouds derived from laser scanning sensors or photogrammetry;
New methods for classification and segmentation for large data sets of point clouds;
Methods for reconstructing 3D objects;
Frameworks for 3D object modeling in urban application, construction monitoring, and transportation infrastructures;
New strategies to evaluate results from point cloud processing.

Dr. Linh TRUONG-HONG
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. Remote Sensing 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 2700 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.

Published Papers (5 papers)

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Research

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19 pages, 7428 KiB

Open AccessArticle

A Comparative Study about Data Structures Used for Efficient Management of Voxelised Full-Waveform Airborne LiDAR Data during 3D Polygonal Model Creation

by Milto Miltiadou, Neill D. F. Campbell, Darren Cosker and Michael G. Grant

Remote Sens. 2021, 13(4), 559; https://doi.org/10.3390/rs13040559 - 04 Feb 2021

Cited by 5 | Viewed by 4799

Abstract

In this paper, we investigate the performance of six data structures for managing voxelised full-waveform airborne LiDAR data during 3D polygonal model creation. While full-waveform LiDAR data has been available for over a decade, extraction of peak points is the most widely used approach of interpreting them. The increased information stored within the waveform data makes interpretation and handling difficult. It is, therefore, important to research which data structures are more appropriate for storing and interpreting the data. In this paper, we investigate the performance of six data structures while voxelising and interpreting full-waveform LiDAR data for 3D polygonal model creation. The data structures are tested in terms of time efficiency and memory consumption during run-time and are the following: (1) 1D-Array that guarantees coherent memory allocation, (2) Voxel Hashing, which uses a hash table for storing the intensity values (3) Octree (4) Integral Volumes that allows finding the sum of any cuboid area in constant time, (5) Octree Max/Min, which is an upgraded octree and (6) Integral Octree, which is proposed here and it is an attempt to combine the benefits of octrees and Integral Volumes. In this paper, it is shown that Integral Volumes is the more time efficient data structure but it requires the most memory allocation. Furthermore, 1D-Array and Integral Volumes require the allocation of coherent space in memory including the empty voxels, while Voxel Hashing and the octree related data structures do not require to allocate memory for empty voxels. These data structures, therefore, and as shown in the test conducted, allocate less memory. To sum up, there is a need to investigate how the LiDAR data are stored in memory. Each tested data structure has different benefits and downsides; therefore, each application should be examined individually. Full article

(This article belongs to the Special Issue Lidar Remote Sensing in 3D Object Modelling)

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26 pages, 8610 KiB

Open AccessArticle

Deep Localization of Static Scans in Mobile Mapping Point Clouds

by Yufu Zang, Fancong Meng, Roderik Lindenbergh, Linh Truong-Hong and Bijun Li

Remote Sens. 2021, 13(2), 219; https://doi.org/10.3390/rs13020219 - 10 Jan 2021

Cited by 2 | Viewed by 2207

Abstract

Mobile laser scanning (MLS) systems are often used to efficiently acquire reference data covering a large-scale scene. The terrestrial laser scanner (TLS) can easily collect high point density data of local scene. Localization of static TLS scans in mobile mapping point clouds can afford detailed geographic information for many specific tasks especially in autonomous driving and robotics. However, large-scale MLS reference data often have a huge amount of data and many similar scene data; significant differences may exist between MLS and TLS data. To overcome these challenges, this paper presents a novel deep neural network-based localization method in urban environment, divided by place recognition and pose refinement. Firstly, simple, reliable primitives, cylinder-like features were extracted to describe the global features of a local urban scene. Then, a probabilistic framework is applied to estimate a similarity between TLS and MLS data, under a stable decision-making strategy. Based on the results of a place recognition, we design a patch-based convolution neural network (CNN) (point-based CNN is used as kernel) for pose refinement. The input data unit is the batch consisting of several patches. One patch goes through three main blocks: feature extraction block (FEB), the patch correspondence search block and the pose estimation block. Finally, a global refinement was proposed to tune the predicted transformation parameters to realize localization. The research aim is to find the most similar scene of MLS reference data compared with the local TLS scan, and accurately estimate the transformation matrix between them. To evaluate the performance, comprehensive experiments were carried out. The experiments demonstrate that the proposed method has good performance in terms of efficiency, i.e., the runtime of processing a million points is 5 s, robustness, i.e., the success rate of place recognition is 100% in the experiments, accuracy, i.e., the mean rotation and translation error is (0.24 deg, 0.88 m) and (0.03 deg, 0.06 m) on TU Delft campus and Shanghai urban datasets, respectively, and outperformed some commonly used methods (e.g., iterative closest point (ICP), coherent point drift (CPD), random sample consensus (RANSAC)-based method). Full article

(This article belongs to the Special Issue Lidar Remote Sensing in 3D Object Modelling)

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20 pages, 30250 KiB

Open AccessArticle

Building Component Detection on Unstructured 3D Indoor Point Clouds Using RANSAC-Based Region Growing

by Sangmin Oh, Dongmin Lee, Minju Kim, Taehoon Kim and Hunhee Cho

Remote Sens. 2021, 13(2), 161; https://doi.org/10.3390/rs13020161 - 06 Jan 2021

Cited by 13 | Viewed by 3470

Abstract

With the advancement of light detection and ranging (LiDAR) technology, the mobile laser scanner (MLS) has been regarded as an important technology to collect geometric representations of the indoor environment. In particular, methods for detecting indoor objects from indoor point cloud data (PCD) captured through MLS have thus far been developed based on the trajectory of MLS. However, the existing methods have a limitation on applying to an indoor environment where the building components made by concrete impede obtaining the information of trajectory. Thus, this study aims to propose a building component detection algorithm for MLS-based indoor PCD without trajectory using random sample consensus (RANSAC)-based region growth. The proposed algorithm used the RANSAC and region growing to overcome the low accuracy and uniformity of MLS caused by the movement of LiDAR. This study ensures over 90% precision, recall, and proper segmentation rate of building component detection by testing the algorithm using the indoor PCD. The result of the case study shows that the proposed algorithm opens the possibility of accurately detecting interior objects from indoor PCD without trajectory information of MLS. Full article

(This article belongs to the Special Issue Lidar Remote Sensing in 3D Object Modelling)

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Review

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34 pages, 5140 KiB

Open AccessReview

A Decade of Modern Bridge Monitoring Using Terrestrial Laser Scanning: Review and Future Directions

by Maria Rashidi, Masoud Mohammadi, Saba Sadeghlou Kivi, Mohammad Mehdi Abdolvand, Linh Truong-Hong and Bijan Samali

Remote Sens. 2020, 12(22), 3796; https://doi.org/10.3390/rs12223796 - 19 Nov 2020

Cited by 106 | Viewed by 9354

Abstract

Over the last decade, particular interest in using state-of-the-art emerging technologies for inspection, assessment, and management of civil infrastructures has remarkably increased. Advanced technologies, such as laser scanners, have become a suitable alternative for labor intensive, expensive, and unsafe traditional inspection and maintenance methods, which encourage the increasing use of this technology in construction industry, especially in bridges. This paper aims to provide a thorough mixed scientometric and state-of-the-art review on the application of terrestrial laser scanners (TLS) in bridge engineering and explore investigations and recommendations of researchers in this area. Following the review, more than 1500 research publications were collected, investigated and analyzed through a two-fold literature search published within the last decade from 2010 to 2020. Research trends, consisting of dominated sub-fields, co-occurrence of keywords, network of researchers and their institutions, along with the interaction of research networks, were quantitatively analyzed. Moreover, based on the collected papers, application of TLS in bridge engineering and asset management was reviewed according to four categories including (1) generation of 3D model, (2) quality inspection, (3) structural assessment, and (4) bridge information modeling (BrIM). Finally, the paper identifies the current research gaps, future directions obtained from the quantitative analysis, and in-depth discussions of the collected papers in this area. Full article

(This article belongs to the Special Issue Lidar Remote Sensing in 3D Object Modelling)

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Other

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14 pages, 5680 KiB

Open AccessTechnical Note

Framework for 3D Point Cloud Modelling Aimed at Road Sight Distance Estimations

by Keila González-Gómez, Luis Iglesias, Roberto Rodríguez-Solano and María Castro

Remote Sens. 2019, 11(23), 2730; https://doi.org/10.3390/rs11232730 - 20 Nov 2019

Cited by 3 | Viewed by 2837

Abstract

Existing roads require periodic evaluation in order to ensure safe transportation. Estimations of the available sight distance (ASD) are fundamental to make sure motorists have sufficient visibility to perform basic driving tasks. Mobile LiDAR Systems (MLS) can provide these evaluations with accurate three-dimensional models of the road and surroundings. Similarly, Geographic Information System (GIS) tools have been employed to obtain ASD. Due to the fact that widespread GIS formats used to store digital surface models handle elevation as an attribute of location, the presented methodology has separated the representation of ground and aboveground elements. The road geometry and surrounding ground are stored in digital terrain models (DTM). Correspondingly, abutting vegetation, manmade structures, road assets and other roadside elements are stored in 3D objects and placed on top of the DTM. Both the DTM and 3D objects are accurately obtained from a denoised and classified LiDAR point cloud. Based on the consideration that roadside utilities and most manmade structures are well-defined geometric elements, some visual obstructions are extracted and/or replaced with 3D objects from online warehouses. Different evaluations carried out with this method highlight the tradeoff between the accuracy of the estimations, performance and geometric complexity as well as the benefits of the individual consideration of road assets. Full article

(This article belongs to the Special Issue Lidar Remote Sensing in 3D Object Modelling)

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