scholarly journals MODELLING PERMAFROST TERRAIN USING KINEMATIC, DUAL-WAVELENGTH LASER SCANNING

2020 ◽  
Vol V-2-2020 ◽  
pp. 749-756
Author(s):  
A. Kukko ◽  
H. Kaartinen ◽  
G. Osinski ◽  
J. Hyyppä
Keyword(s):  
Standard Deviation ◽  
Data Processing ◽  
Point Cloud ◽  
Laser Scanning ◽  
Base Station ◽  
Dual Wavelength ◽  
The Arctic ◽  
Point Cloud Data ◽  
Cloud Data ◽  
3D Data

Abstract. In this paper we introduce the first dual-wavelength, kinematic backpack laser scanning system and its application on high resolution 3D terrain modelling of permafrost landforms. We discuss the data processing pipeline from acquisition to preparation, system calibration and terrain model process. Topographic information is vital for planning and monitoring tasks in urban planning, road construction for mass calculations, and mitigation of flood and wind related risks by structural design in coastal areas. 3D data gives possibility to understand natural processes inducing changes in the terrain, such as the cycles of thaw-freeze in permafrost regions. Through an application case on permafrost landforms in the Arctic we present the field practices and data processing applied, characterize the data output and discuss the precision and accuracy of the base station, trajectory and point cloud data. Two pulsed time of flight ranging, high performance mobile laser scanners were used in combination with a near navigation grade GNSS-IMU positioning on a kinematic backpack platform. The study shows that with a high-end system 15 mm absolute accuracy of 3D data could be achieved using PPP processing for the GNSS base station and multi-pass differential trajectory post-processing. The PPP solution shows millimetre level agreement (Easting 6 mm, Northing 4 mm, and elevation 8 mm standard deviations) for the base station coordinates over an 11 day period. The point cloud residual standard deviation for angular boresight misalignment was 27 mm. The absolute distance between ground surfaces from interactive analysis was 17 mm with 13 mm standard deviation (n = 64). The proposed backpack laser scanning provides accurate and precise 3D data and performance over considerable land surface area for detailed elevation modelling and analysis of the morphology of features of interest. The high density point cloud data permits fusion of the dual-wavelength lidar reflectance data into spectral products.

scholarly journals Point Cloud Data Retrieval from 3D Geospatial Database for Automated Road Median Extraction

2018 ◽  
Author(s):  
Pankaj Kumar ◽  
Paul Lewis ◽  
Conor P. McElhinney
Keyword(s):  
Data Processing ◽  
Point Cloud ◽  
Laser Scanning ◽  
Data Retrieval ◽  
Lidar Data ◽  
Point Cloud Data ◽  
Cloud Data ◽  
The Road ◽  
Efficient Storage ◽  
Geospatial Database

Laser scanning systems make use of Light Detection and Ranging (LiDAR) technology to acquire accurately georeferenced sets of dense 3D point cloud data. The information acquired using these systems produces better knowledge about the terrain objects which are inherently 3D in nature. The LiDAR data acquired from mobile, airborne or terrestrial platforms provides several benefit over conventional sources of data acquisition in terms of accuracy, resolution and attributes. However, the large volume and scale of LiDAR data have inhibited the development of automated feature extraction algorithms due to the extensive computational cost involved in it. Moreover, the heterogeneously distributed point cloud, which represents objects with varying size, point density, holes and complicated structures pose a great challenge for data processing. Currently, geospatial database systems do not provide a robust solution for efficient storage and accessibility of raw data in a way that data processing could be applied based on optimal spatial extent. In this paper, we present Global LiDAR and Imagery Mobile Processing Spatial Environment (GLIMPSE) system that provides a framework for storage, management and integration of 3D LiDAR data acquired from multiple platforms. The system facilitates an efficient accessibility to the raw dataset, which is hierarchically represented in a geographically meaningful way. We utilise the GLIMPSE system to automatically extract road median from Airborne Laser Scanning (ALS) point cloud. In the first part of this paper, we detail an approach to efficiently retrieve the point cloud data from the GLIMPSE system for a particular geographic area based on user requirements. In the second part, we present an algorithm to automatically extract road median from the retrieved LiDAR data. The developed road median extraction algorithm utilises the LiDAR elevation and intensity attributes to distinguish the median from the road surface. We successfully tested our algorithms on two road sections consisting of distinct road median types based on concrete and grass-hedge barriers. The use of GLIMPSE improved the efficiency of the road median extraction in terms of fast accessibility to ALS point cloud data for the required road sections. The developed system and its associated algorithms provide a comprehensive solution to the user's requirement for an efficient storage, integration, retrieval and processing of large volumes of LiDAR point cloud data. These findings and knowledge contribute to a more rapid, cost-effective and comprehensive approach to surveying road networks.

scholarly journals An Efficient Filtering Approach for Removing Outdoor Point Cloud Data of Manhattan-World Buildings

2021 ◽  
Vol 13 (19) ◽  
pp. 3796
Author(s):  
Lei Fan ◽  
Yuanzhi Cai
Keyword(s):  
Point Cloud ◽  
Indoor Environment ◽  
Laser Scanning ◽  
Point Cloud Data ◽  
Hole Filling ◽  
Cloud Data ◽  
Indoor Space ◽  
Wide Range ◽  
Data Points ◽  
Filtering Approach

Laser scanning is a popular means of acquiring the indoor scene data of buildings for a wide range of applications concerning indoor environment. During data acquisition, unwanted data points beyond the indoor space of interest can also be recorded due to the presence of openings, such as windows and doors on walls. For better visualization and further modeling, it is beneficial to filter out those data, which is often achieved manually in practice. To automate this process, an efficient image-based filtering approach was explored in this research. In this approach, a binary mask image was created and updated through mathematical morphology operations, hole filling and connectively analysis. The final mask obtained was used to remove the data points located outside the indoor space of interest. The application of the approach to several point cloud datasets considered confirms its ability to effectively keep the data points in the indoor space of interest with an average precision of 99.50%. The application cases also demonstrate the computational efficiency (0.53 s, at most) of the approach proposed.

scholarly journals VISUALIZATION OF THE CONSTRUCTION OF ANCIENT ROMAN BUILDINGS IN OSTIA USING POINT CLOUD DATA

2017 ◽  
Vol XLII-2/W3 ◽  
pp. 345-352
Author(s):  
Y. Hori ◽  
T. Ogawa
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Feature Matching ◽  
Three Dimensional ◽  
Point Clouds ◽  
Final Report ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Coverage Accuracy ◽  
New Research

The implementation of laser scanning in the field of archaeology provides us with an entirely new dimension in research and surveying. It allows us to digitally recreate individual objects, or entire cities, using millions of three-dimensional points grouped together in what is referred to as "point clouds". In addition, the visualization of the point cloud data, which can be used in the final report by archaeologists and architects, should usually be produced as a JPG or TIFF file. Not only the visualization of point cloud data, but also re-examination of older data and new survey of the construction of Roman building applying remote-sensing technology for precise and detailed measurements afford new information that may lead to revising drawings of ancient buildings which had been adduced as evidence without any consideration of a degree of accuracy, and finally can provide new research of ancient buildings. We used laser scanners at fields because of its speed, comprehensive coverage, accuracy and flexibility of data manipulation. Therefore, we “skipped” many of post-processing and focused on the images created from the meta-data simply aligned using a tool which extended automatic feature-matching algorithm and a popular renderer that can provide graphic results.

scholarly journals From Point Cloud Data to Building Information Modelling: An Automatic Parametric Workflow for Heritage

2020 ◽  
Vol 12 (7) ◽  
pp. 1094 ◽  
Author(s):  
Mesrop Andriasyan ◽  
Juan Moyano ◽  
Juan Enrique Nieto-Julián ◽  
Daniel Antón
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Accuracy Assessment ◽  
Parametric Modelling ◽  
Point Cloud Data ◽  
Building Information Modelling ◽  
Historical Interpretation ◽  
Information Modelling ◽  
Cloud Data ◽  
Building Information

Building Information Modelling (BIM) is a globally adapted methodology by government organisations and builders who conceive the integration of the organisation, planning, development and the digital construction model into a single project. In the case of a heritage building, the Historic Building Information Modelling (HBIM) approach is able to cover the comprehensive restoration of the building. In contrast to BIM applied to new buildings, HBIM can address different models which represent either periods of historical interpretation, restoration phases or records of heritage assets over time. Great efforts are currently being made to automatically reconstitute the geometry of cultural heritage elements from data acquisition techniques such as Terrestrial Laser Scanning (TLS) or Structure From Motion (SfM) into BIM (Scan-to-BIM). Hence, this work advances on the parametric modelling from remote sensing point cloud data, which is carried out under the Rhino+Grasshopper-ArchiCAD combination. This workflow enables the automatic conversion of TLS and SFM point cloud data into textured 3D meshes and thus BIM objects to be included in the HBIM project. The accuracy assessment of this workflow yields a standard deviation value of 68.28 pixels, which is lower than other author’s precision but suffices for the automatic HBIM of the case study in this research.

Design of Light-Small Mobile 3D Laser Measurement System

2013 ◽  
Vol 405-408 ◽  
pp. 3032-3036
Author(s):  
Yi Bo Sun ◽  
Xin Qi Zheng ◽  
Zong Ren Jia ◽  
Gang Ai
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Digital Camera ◽  
Scanning System ◽  
Point Cloud Data ◽  
3D Laser Scanning ◽  
Large Area ◽  
Cloud Data ◽  
Laser Scanning System

At present, most of the commercial 3D laser scanning measurement systems do work for a large area and a big scene, but few shows their advantage in the small area or small scene. In order to solve this shortage, we design a light-small mobile 3D laser scanning system, which integrates GPS, INS, laser scanner and digital camera and other sensors, to generate the Point Cloud data of the target through data filtering and fusion. This system can be mounted on airborne or terrestrial small mobile platform and enables to achieve the goal of getting Point Cloud data rapidly and reconstructing the real 3D model. Compared to the existing mobile 3D laser scanning system, the system we designed has high precision but lower cost, smaller hardware and more flexible.

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