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Special Issue "Mobile Laser Scanning Systems"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors, Control, and Telemetry".

Deadline for manuscript submissions: closed (31 July 2019).

Special Issue Editors

Guest Editor
Prof. Dr. Jonathan Li

Department of Geography and Environmental Management and the Department of Systems Design Engineering (cross-appointed), University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L 3G1, Canada
Website 1 | Website 2 | E-Mail
Phone: 6479686898
Interests: mobile laser scanners; multispectral LiDAR; LiDAR data processing; LiDAR backpack; LiDAR modeling; indoor mapping
Guest Editor
Prof. Dr. Ayman F. Habib

Purdue University, Lyles School of Civil Engineering, 550 Stadium Mall Drive, HAMP 4108, West Lafayette, IN 47907, USA
Website | E-Mail
Interests: Photogrammetry, Laser scanning, Mobile Mapping Systems, System Calibration, Computer Vision, Unmanned Aerial Mapping Systems, and Multi-Sensor/Multi-platform data integration
Guest Editor
A/Prof. Chenglu Wen

Department of Cognitive Science, Xiamen University, 422 Siming Road South, Xiamen 361005, China
Website | E-Mail
Interests: 3D point cloud processing; LiDAR remote sensing; multi-sensor data fusion; image/signal processing; robot mapping

Special Issue Information

Dear Colleagues,

Three-dimensional data is a fundamental and essential part of a growing number of applications ranging from urban planning, cultural heritage documentation, intelligent transportation systems, autonomous driving, smart cities, to indoor/outdoor disaster simulation. Mobile laser scanning systems (including airborne, vehicle-borne, handheld and backpack systems), which provide geo-referenced high-density 3D point cloud data, have become an alternative powerful data source of 3D geospatial information. This Special Issue not only covers the traditional remaining challenges (multi-sensor calibration, multisource data registration, and 3D point cloud processing) in mobile laser scanning systems, but also focuses on solutions, methods and algorithms for low-cost sensor integration and mobile localization and mapping in GNSS-denied environments.

The aim of this Special Issue is to present the state-of-the-art research and development in mobile laser scanning systems. We would like to invite contributions on the following topics (but it is not limited to them):

  • Low-cost mobile laser scanning systems
  • Wearble mobile laser scanning systems
  • Multi-/hyper-spectral laser scanning systems
  • Multi-sensor calibration and data fusion
  • Multisource data registration
  • Low-cost sensor integration and fusion
  • Machine/deep learning approaches to point cloud processing
  • Quality evaluation and control of mobile laser scanning data
  • 3D object detection and recognition from mobile laser scanning data
  • AI-based algorithms for the automated conversion of point clouds into HD maps
  • 3D mapping in GNSS-denied environments
  • Novel applications of mobile laser scanning systems

Prof. Dr. Jonathan Li
Prof. Dr. Ayman Habib
A/Prof. Chenglu Wen
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 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 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

  • Remote sensing
  • LiDAR
  • Laser scanning
  • Indoor mobile laser scanning
  • Point cloud
  • Sensor calibration and data fusion
  • Feature extraction
  • Road inventory
  • 3D modeling
  • 3D object detection and recognition

Published Papers (2 papers)

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Research

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Open AccessArticle
Extracting Diameter at Breast Height with a Handheld Mobile LiDAR System in an Outdoor Environment
Sensors 2019, 19(14), 3212; https://doi.org/10.3390/s19143212
Received: 29 May 2019 / Revised: 8 July 2019 / Accepted: 18 July 2019 / Published: 21 July 2019
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Abstract
Mobile laser scanning (MLS) is widely used in the mapping of forest environments. It has become important for extracting the parameters of forest trees using the generated environmental map. In this study, a three-dimensional point cloud map of a forest area was generated [...] Read more.
Mobile laser scanning (MLS) is widely used in the mapping of forest environments. It has become important for extracting the parameters of forest trees using the generated environmental map. In this study, a three-dimensional point cloud map of a forest area was generated by using the Velodyne VLP-16 LiDAR system, so as to extract the diameter at breast height (DBH) of individual trees. The Velodyne VLP-16 LiDAR system and inertial measurement units (IMU) were used to construct a mobile measurement platform for generating 3D point cloud maps for forest areas. The 3D point cloud map in the forest area was processed offline, and the ground point cloud was removed by the random sample consensus (RANSAC) algorithm. The trees in the experimental area were segmented by the European clustering algorithm, and the DBH component of the tree point cloud was extracted and projected onto a 2D plane, fitting the DBH of the trees using the RANSAC algorithm in the plane. A three-dimensional point cloud map of 71 trees was generated in the experimental area, and estimated the DBH. The mean and variance of the absolute error were 0.43 cm and 0.50, respectively. The relative error of the whole was 2.27%, the corresponding variance was 15.09, and the root mean square error (RMSE) was 0.70 cm. The experimental results were good and met the requirements of forestry mapping, and the application value and significance were presented. Full article
(This article belongs to the Special Issue Mobile Laser Scanning Systems)
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Review

Jump to: Research

Open AccessReview
Object Recognition, Segmentation, and Classification of Mobile Laser Scanning Point Clouds: A State of the Art Review
Sensors 2019, 19(4), 810; https://doi.org/10.3390/s19040810
Received: 26 December 2018 / Revised: 9 February 2019 / Accepted: 14 February 2019 / Published: 16 February 2019
Cited by 4 | PDF Full-text (6123 KB) | HTML Full-text | XML Full-text
Abstract
Mobile Laser Scanning (MLS) is a versatile remote sensing technology based on Light Detection and Ranging (lidar) technology that has been utilized for a wide range of applications. Several previous reviews focused on applications or characteristics of these systems exist in the literature, [...] Read more.
Mobile Laser Scanning (MLS) is a versatile remote sensing technology based on Light Detection and Ranging (lidar) technology that has been utilized for a wide range of applications. Several previous reviews focused on applications or characteristics of these systems exist in the literature, however, reviews of the many innovative data processing strategies described in the literature have not been conducted in sufficient depth. To this end, we review and summarize the state of the art for MLS data processing approaches, including feature extraction, segmentation, object recognition, and classification. In this review, we first discuss the impact of the scene type to the development of an MLS data processing method. Then, where appropriate, we describe relevant generalized algorithms for feature extraction and segmentation that are applicable to and implemented in many processing approaches. The methods for object recognition and point cloud classification are further reviewed including both the general concepts as well as technical details. In addition, available benchmark datasets for object recognition and classification are summarized. Further, the current limitations and challenges that a significant portion of point cloud processing techniques face are discussed. This review concludes with our future outlook of the trends and opportunities of MLS data processing algorithms and applications. Full article
(This article belongs to the Special Issue Mobile Laser Scanning Systems)
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