Laser Scanning Systems in Landslide Studies

2017 ◽  
pp. 3-19 ◽  
Author(s):  
Biswajeet Pradhan ◽  
Maher Ibrahim Sameen
Keyword(s):  
Laser Scanning ◽  
Scanning Systems

scholarly journals Performance Analysis of Mobile Laser Scanning Systems in Target Representation

2013 ◽  
Vol 5 (7) ◽  
pp. 3140-3155 ◽  
Author(s):  
Yi Lin ◽  
Juha Hyyppä ◽  
Harri Kaartinen ◽  
Antero Kukko
Keyword(s):  
Performance Analysis ◽  
Laser Scanning ◽  
Scanning Systems

Terrestrial Laser Scanning Systems

2020 ◽  
pp. 83-120
Author(s):  
Alojz Kopáčik ◽  
Ján Erdélyi ◽  
Peter Kyrinovič
Keyword(s):  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Scanning Systems

scholarly journals COMPARATIVE GEOMETRICAL INVESTIGATIONS OF HAND-HELD SCANNING SYSTEMS

2016 ◽  
Vol XLI-B5 ◽  
pp. 507-514 ◽  
Author(s):  
T. P. Kersten ◽  
H.-J. Przybilla ◽  
M. Lindstaedt ◽  
F. Tschirschwitz ◽  
M. Misgaiski-Hass
Keyword(s):  
Applied Sciences ◽  
Laser Scanning ◽  
Reference Data ◽  
Test Results ◽  
University Of Applied Sciences ◽  
Humboldt University ◽  
Comprehensive Test ◽  
Structure Sensor ◽  
Scanning Systems ◽  
Test Scenarios

An increasing number of hand-held scanning systems by different manufacturers are becoming available on the market. However, their geometrical performance is little-known to many users. Therefore the Laboratory for Photogrammetry & Laser Scanning of the HafenCity University Hamburg has carried out geometrical accuracy tests with the following systems in co-operation with the Bochum University of Applied Sciences (Laboratory for Photogrammetry) as well as the Humboldt University in Berlin (Institute for Computer Science): DOTProduct DPI-7, Artec Spider, Mantis Vision F5 SR, Kinect v1 + v2, Structure Sensor and Google’s Project Tango. In the framework of these comparative investigations geometrically stable reference bodies were used. The appropriate reference data were acquired by measurement with two structured light projection systems (AICON smartSCAN and GOM ATOS I 2M). The comprehensive test results of the different test scenarios are presented and critically discussed in this contribution.

Boresight alignment method for mobile laser scanning systems

2010 ◽  
Vol 4 (1) ◽  
Author(s):  
P. Rieger ◽  
N. Studnicka ◽  
M. Pfennigbauer ◽  
G. Zach
Keyword(s):  
Laser Scanning ◽  
Alignment Method ◽  
Scanning Systems

scholarly journals Mobile Laser Scanning Systems for Measuring the Clearance Gauge of Railways: State of Play, Testing and Outlook

Sensors ◽  
2016 ◽  
Vol 16 (5) ◽  
pp. 683 ◽  
Author(s):  
Sławomir Mikrut ◽  
Piotr Kohut ◽  
Krystian Pyka ◽  
Regina Tokarczyk ◽  
Tomasz Barszcz ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Scanning Systems

scholarly journals Segmentation of LiDAR Data Using Multilevel Cube Code

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
So-Young Park ◽  
Dae Geon Lee ◽  
Eun Jin Yoo ◽  
Dong-Cheon Lee
Keyword(s):  
Spatial Data ◽  
Laser Scanning ◽  
Point Clouds ◽  
Airborne Laser Scanning ◽  
Lidar Data ◽  
Chain Code ◽  
Cloud Data ◽  
Airborne Laser ◽  
Wide Range ◽  
Scanning Systems

Light detection and ranging (LiDAR) data collected from airborne laser scanning systems are one of the major sources of spatial data. Airborne laser scanning systems have the capacity for rapid and direct acquisition of accurate 3D coordinates. Use of LiDAR data is increasing in various applications, such as topographic mapping, building and city modeling, biomass measurement, and disaster management. Segmentation is a crucial process in the extraction of meaningful information for applications such as 3D object modeling and surface reconstruction. Most LiDAR processing schemes are based on digital image processing and computer vision algorithms. This paper introduces a shape descriptor method for segmenting LiDAR point clouds using a “multilevel cube code” that is an extension of the 2D chain code to 3D space. The cube operator segments point clouds into roof surface patches, including superstructures, removes unnecessary objects, detects the boundaries of buildings, and determines model key points for building modeling. Both real and simulated LiDAR data were used to verify the proposed approach. The experiments demonstrated the feasibility of the method for segmenting LiDAR data from buildings with a wide range of roof types. The method was found to segment point cloud data effectively.

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