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Mobile Laser Scanning Systems
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Mobile Laser Scanning Systems

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

Deadline for manuscript submissions: closed (31 July 2019) | Viewed by 42091

Special Issue Editors

Prof. Dr. Jonathan Li

E-Mail Website
Guest Editor
Geospatial Sensing and Data Intelligence Lab, Faculty of Environment, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada
Interests: LiDAR remote sensing; point cloud understanding; deep learning; 3D vision; HD maps for smart cities and autonomous vehicles
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ayman F. Habib

E-Mail Website
Guest Editor
Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA
Interests: photogrammetry; laser scanning; mobile mapping systems; system calibration; computer vision; unmanned aerial mapping systems; multisensor/multiplatform data integration
Special Issues, Collections and Topics in MDPI journals
Dr. Chenglu Wen

E-Mail Website
Guest Editor
Department of Artificial Intelligence, School of Informatics, Xiamen University, Xiamen 361005, China
Interests: 3D vision; point cloud processing; mobile mapping; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

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

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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

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Published Papers (5 papers)

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Research

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23 pages, 9992 KiB  
Article
Sliding Window Mapping for Omnidirectional RGB-D Sensors
by Nicolas Dalmedico, Marco Antônio Simões Teixeira, Higor Barbosa Santos, Rafael de Castro Martins Nogueira, Lúcia Valéria Ramos de Arruda, Flávio Neves, Jr., Daniel Rodrigues Pipa, Júlio Endress Ramos and André Schneider de Oliveira
Sensors 2019, 19(23), 5121; https://doi.org/10.3390/s19235121 - 22 Nov 2019
Cited by 2 | Viewed by 4483
Abstract
This paper presents an omnidirectional RGB-D (RGB + Distance fusion) sensor prototype using an actuated LIDAR (Light Detection and Ranging) and an RGB camera. Besides the sensor, a novel mapping strategy is developed considering sensor scanning characteristics. The sensor can gather RGB and [...] Read more.
This paper presents an omnidirectional RGB-D (RGB + Distance fusion) sensor prototype using an actuated LIDAR (Light Detection and Ranging) and an RGB camera. Besides the sensor, a novel mapping strategy is developed considering sensor scanning characteristics. The sensor can gather RGB and 3D data from any direction by toppling in 90 degrees a laser scan sensor and rotating it about its central axis. The mapping strategy is based on two environment maps, a local map for instantaneous perception, and a global map for perception memory. The 2D local map represents the surface in front of the robot and may contain RGB data, allowing environment reconstruction and human detection, similar to a sliding window that moves with a robot and stores surface data. Full article
(This article belongs to the Special Issue Mobile Laser Scanning Systems)
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17 pages, 10370 KiB  
Article
Spatial Aggregation Net: Point Cloud Semantic Segmentation Based on Multi-Directional Convolution
by Guorong Cai, Zuning Jiang, Zongyue Wang, Shangfeng Huang, Kai Chen, Xuyang Ge and Yundong Wu
Sensors 2019, 19(19), 4329; https://doi.org/10.3390/s19194329 - 7 Oct 2019
Cited by 9 | Viewed by 3982
Abstract
Semantic segmentation of 3D point clouds plays a vital role in autonomous driving, 3D maps, and smart cities, etc. Recent work such as PointSIFT shows that spatial structure information can improve the performance of semantic segmentation. Motivated by this phenomenon, we propose Spatial [...] Read more.
Semantic segmentation of 3D point clouds plays a vital role in autonomous driving, 3D maps, and smart cities, etc. Recent work such as PointSIFT shows that spatial structure information can improve the performance of semantic segmentation. Motivated by this phenomenon, we propose Spatial Aggregation Net (SAN) for point cloud semantic segmentation. SAN is based on multi-directional convolution scheme that utilizes the spatial structure information of point cloud. Firstly, Octant-Search is employed to capture the neighboring points around each sampled point. Secondly, we use multi-directional convolution to extract information from different directions of sampled points. Finally, max-pooling is used to aggregate information from different directions. The experimental results conducted on ScanNet database show that the proposed SAN has comparable results with state-of-the-art algorithms such as PointNet, PointNet++, and PointSIFT, etc. In particular, our method has better performance on flat, small objects, and the edge areas that connect objects. Moreover, our model has good trade-off in segmentation accuracy and time complexity. Full article
(This article belongs to the Special Issue Mobile Laser Scanning Systems)
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35 pages, 23573 KiB  
Article
Indoor and Outdoor Backpack Mapping with Calibrated Pair of Velodyne LiDARs
by Martin Velas, Michal Spanel, Tomas Sleziak, Jiri Habrovec and Adam Herout
Sensors 2019, 19(18), 3944; https://doi.org/10.3390/s19183944 - 12 Sep 2019
Cited by 23 | Viewed by 7594
Abstract
This paper presents a human-carried mapping backpack based on a pair of Velodyne LiDAR scanners. Our system is a universal solution for both large scale outdoor and smaller indoor environments. It benefits from a combination of two LiDAR scanners, which makes the odometry [...] Read more.
This paper presents a human-carried mapping backpack based on a pair of Velodyne LiDAR scanners. Our system is a universal solution for both large scale outdoor and smaller indoor environments. It benefits from a combination of two LiDAR scanners, which makes the odometry estimation more precise. The scanners are mounted under different angles, thus a larger space around the backpack is scanned. By fusion with GNSS/INS sub-system, the mapping of featureless environments and the georeferencing of resulting point cloud is possible. By deploying SoA methods for registration and the loop closure optimization, it provides sufficient precision for many applications in BIM (Building Information Modeling), inventory check, construction planning, etc. In our indoor experiments, we evaluated our proposed backpack against ZEB-1 solution, using FARO terrestrial scanner as the reference, yielding similar results in terms of precision, while our system provides higher data density, laser intensity readings, and scalability for large environments. Full article
(This article belongs to the Special Issue Mobile Laser Scanning Systems)
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16 pages, 6907 KiB  
Article
Extracting Diameter at Breast Height with a Handheld Mobile LiDAR System in an Outdoor Environment
by Sanzhang Zhou, Feng Kang, Wenbin Li, Jiangming Kan, Yongjun Zheng and Guojian He
Sensors 2019, 19(14), 3212; https://doi.org/10.3390/s19143212 - 21 Jul 2019
Cited by 43 | Viewed by 4918
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

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42 pages, 6123 KiB  
Review
Object Recognition, Segmentation, and Classification of Mobile Laser Scanning Point Clouds: A State of the Art Review
by Erzhuo Che, Jaehoon Jung and Michael J. Olsen
Sensors 2019, 19(4), 810; https://doi.org/10.3390/s19040810 - 16 Feb 2019
Cited by 207 | Viewed by 19685
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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