scholarly journals Impact-Detection Algorithm That Uses Point Clouds as Topographic Inputs for 3D Rockfall Simulations

Geosciences ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 188
Author(s):  
François Noël ◽  
Catherine Cloutier ◽  
Michel Jaboyedoff ◽  
Jacques Locat
Keyword(s):  
Surface Roughness ◽  
Digital Terrain Model ◽  
Simulation Models ◽  
Point Clouds ◽  
Detection Algorithm ◽  
Simulation Software ◽  
Impact Detection ◽  
3D Point Clouds ◽  
Terrain Models ◽  
Rockfall Simulation

Numerous 3D rockfall simulation models use coarse gridded digital terrain model (DTM raster) as their topography input. Artificial surface roughness is often added to overcome the loss of details that occurs during the gridding process. Together with the use of sensitive energy damping parameters, they provide great freedom to the user at the expense of the objectivity of the method. To quantify and limit the range of such artificial values, we developed an impact-detection algorithm that can be used to extract the perceived surface roughness from detailed terrain samples in relation to the size of the impacting rocks. The algorithm can also be combined with a rebound model to perform rockfall simulations directly on detailed 3D point clouds. The abilities of the algorithm are demonstrated by objectively extracting different perceived surface roughnesses from detailed terrain samples and by simulating rockfalls on detailed terrain models as proof of concept. The results produced are also compared to that of rockfall simulation software CRSP 4, RocFall 8 and Rockyfor3D 5.2.15 as validation. Although differences were observed, the validation shows that the algorithm can produce similar results. With the presented approach not being limited to coarse terrain models, the need for adding artificial terrain roughness or for adjusting sensitive damping parameters on a per-site basis is reduced, thereby limiting the related biases and subjectivity.

scholarly journals Comparative Analysis of Different Mobile LiDAR Mapping Systems for Ditch Line Characterization

2021 ◽  
Vol 13 (13) ◽  
pp. 2485
Author(s):  
Yi-Chun Lin ◽  
Raja Manish ◽  
Darcy Bullock ◽  
Ayman Habib
Keyword(s):  
Flow Direction ◽  
Sediment Accumulation ◽  
Digital Terrain Model ◽  
Vertical Direction ◽  
Point Clouds ◽  
Lidar Data ◽  
Cross Sectional ◽  
Premature Failure ◽  
3D Point Clouds ◽  
Mapping Systems

Maintenance of roadside ditches is important to avoid localized flooding and premature failure of pavements. Scheduling effective preventative maintenance requires a reasonably detailed mapping of the ditch profile to identify areas in need of excavation to remove long-term sediment accumulation. This study utilizes high-resolution, high-quality point clouds collected by mobile LiDAR mapping systems (MLMS) for mapping roadside ditches and performing hydrological analyses. The performance of alternative MLMS units, including an unmanned aerial vehicle, an unmanned ground vehicle, a portable backpack system along with its vehicle-mounted version, a medium-grade wheel-based system, and a high-grade wheel-based system, is evaluated. Point clouds from all the MLMS units are in agreement within the ±3 cm range for solid surfaces and ±7 cm range for vegetated areas along the vertical direction. The portable backpack system that could be carried by a surveyor or mounted on a vehicle is found to be the most cost-effective method for mapping roadside ditches, followed by the medium-grade wheel-based system. Furthermore, a framework for ditch line characterization is proposed and tested using datasets acquired by the medium-grade wheel-based and vehicle-mounted portable systems over a state highway. An existing ground-filtering approach—cloth simulation—is modified to handle variations in point density of mobile LiDAR data. Hydrological analyses, including flow direction and flow accumulation, are applied to extract the drainage network from the digital terrain model (DTM). Cross-sectional/longitudinal profiles of the ditch are automatically extracted from the LiDAR data and visualized in 3D point clouds and 2D images. The slope derived from the LiDAR data turned out to be very close to the highway cross slope design standards of 2% on driving lanes, 4% on shoulders, and a 6-by-1 slope for ditch lines.

Extracting Geometric Edges from 3D Point Clouds Based on Normal Vector Change

2014 ◽  
Vol 571-572 ◽  
pp. 729-734
Author(s):  
Jia Li ◽  
Huan Lin ◽  
Duo Qiang Zhang ◽  
Xiao Lu Xue
Keyword(s):  
Point Clouds ◽  
Detection Algorithm ◽  
Growth Strategy ◽  
Normal Vector ◽  
Geometric Morphometric ◽  
3D Point Clouds ◽  
Real Scene ◽  
Edge Points ◽  
Normal Vectors ◽  
Change Intensity

Normal vector of 3D surface is important differential geometric property over localized neighborhood, and its abrupt change along the surface directly reflects the variation of geometric morphometric. Based on this observation, this paper presents a novel edge detection algorithm in 3D point clouds, which utilizes the change intensity and change direction of adjacent normal vectors and is composed of three steps. First, a two-dimensional grid is constructed according to the inherent data acquisition sequence so as to build up the topology of points. Second, by this topological structure preliminary edge points are retrieved, and the potential directions of edges passing through them are estimated according to the change of normal vectors between adjacent points. Finally, an edge growth strategy is designed to regain the missing edge points and connect them into complete edge lines. The results of experiment in a real scene demonstrate that the proposed algorithm can extract geometric edges from 3D point clouds robustly, and is able to reduce edge quality’s dependence on user defined parameters.

scholarly journals AUTOMATED DETECTION AND CLOSING OF HOLES IN AERIAL POINT CLOUDS USING AN UAS

2017 ◽  
Vol XLII-2/W6 ◽  
pp. 101-107
Author(s):  
T. Fiolka ◽  
F. Rouatbi ◽  
D. Bender
Keyword(s):  
Point Cloud ◽  
Ground Plane ◽  
Point Clouds ◽  
Current Approach ◽  
Automated Detection ◽  
Lidar System ◽  
3D Point Clouds ◽  
Terrain Models ◽  
3D Terrain ◽  
Flight Route

3D terrain models are an important instrument in areas like geology, agriculture and reconnaissance. Using an automated UAS with a line-based LiDAR can create terrain models fast and easily even from large areas. But the resulting point cloud may contain holes and therefore be incomplete. This might happen due to occlusions, a missed flight route due to wind or simply as a result of changes in the ground height which would alter the swath of the LiDAR system. This paper proposes a method to detect holes in 3D point clouds generated during the flight and adjust the course in order to close them. First, a grid-based search for holes in the horizontal ground plane is performed. Then a check for vertical holes mainly created by buildings walls is done. Due to occlusions and steep LiDAR angles, closing the vertical gaps may be difficult or even impossible. Therefore, the current approach deals with holes in the ground plane and only marks the vertical holes in such a way that the operator can decide on further actions regarding them. The aim is to efficiently create point clouds which can be used for the generation of complete 3D terrain models.

scholarly journals Automatic 2D Floorplan CAD Generation from 3D Point Clouds

2020 ◽  
Vol 10 (8) ◽  
pp. 2817 ◽  
Author(s):  
Uuganbayar Gankhuyag ◽  
Ji-Hyeong Han
Keyword(s):  
Point Clouds ◽  
Detection Algorithm ◽  
Information Modeling ◽  
Ongoing Research ◽  
Cloud Data ◽  
Industry Foundation Classes ◽  
Automated Method ◽  
3D Point Clouds ◽  
Building Information ◽  
Geometric Quality

In the architecture, engineering, and construction (AEC) industry, creating an indoor model of existing buildings has been a challenging task since the introduction of building information modeling (BIM). Because the process of BIM is primarily manual and implies a high possibility of error, the automated creation of indoor models remains an ongoing research. In this paper, we propose a fully automated method to generate 2D floorplan computer-aided designs (CADs) from 3D point clouds. The proposed method consists of two main parts. The first is to detect planes in buildings, such as walls, floors, and ceilings, from unstructured 3D point clouds and to classify them based on the Manhattan-World (MW) assumption. The second is to generate 3D BIM in the industry foundation classes (IFC) format and a 2D floorplan CAD using the proposed line-detection algorithm. We experimented the proposed method on 3D point cloud data from a university building, residential houses, and apartments and evaluated the geometric quality of a wall reconstruction. We also offer the source code for the proposed method on GitHub.

scholarly journals Fast Planar Detection System Using a GPU-Based 3D Hough Transform for LiDAR Point Clouds

2020 ◽  
Vol 10 (5) ◽  
pp. 1744 ◽  
Author(s):  
Yifei Tian ◽  
Wei Song ◽  
Long Chen ◽  
Yunsick Sung ◽  
Jeonghoon Kwak ◽  
...  
Keyword(s):  
Hough Transform ◽  
Detection System ◽  
Point Clouds ◽  
Detection Algorithm ◽  
Polar Coordinates ◽  
Processing Unit ◽  
3D Point Clouds ◽  
Hough Space ◽  
Plane Extraction ◽  
3D Hough Transform

Plane extraction is regarded as a necessary function that supports judgment basis in many applications, including semantic digital map reconstruction and path planning for unmanned ground vehicles. Owing to the heterogeneous density and unstructured spatial distribution of three-dimensional (3D) point clouds collected by light detection and ranging (LiDAR), plane extraction from it is recently a significant challenge. This paper proposed a parallel 3D Hough transform algorithm to realize rapid and precise plane detection from 3D LiDAR point clouds. After transforming all the 3D points from a Cartesian coordinate system to a pre-defined 3D Hough space, the generated Hough space is rasterised into a series of arranged cells to store the resided point counts into individual cells. A 3D connected component labeling algorithm is developed to cluster the cells with high values in Hough space into several clusters. The peaks from these clusters are extracted so that the targeting planar surfaces are obtained in polar coordinates. Because the laser beams emitted by LiDAR sensor holds several fixed angles, the collected 3D point clouds distribute as several horizontal and parallel circles in plane surfaces. This kind of horizontal and parallel circles mislead plane detecting results from horizontal wall surfaces to parallel planes. For detecting accurate plane parameters, this paper adopts a fraction-to-fraction method to gradually transform raw point clouds into a series of sub Hough space buffers. In our proposed planar detection algorithm, a graphic processing unit (GPU) programming technology is applied to speed up the calculation of 3D Hough space updating and peaks searching.

scholarly journals Estimating forest uniformity in Eucalyptus spp. and Pinus taeda L. stands using field measurements and structure from motion point clouds generated from unmanned aerial vehicle (UAV) data collection

2018 ◽  
Vol 27 (2) ◽  
pp. e005 ◽  
Author(s):  
Ângela M. K. Hentz ◽  
Carlos A. Silva ◽  
Ana P. Dalla Corte ◽  
Sylvio P. Netto ◽  
Michael P. Strager ◽  
...  
Keyword(s):  
Pinus Taeda ◽  
Unmanned Aerial Vehicle ◽  
Digital Terrain Model ◽  
Tree Height ◽  
Point Clouds ◽  
Forest Monitoring ◽  
Surface Model ◽  
3D Point Clouds ◽  
Aerial Vehicle ◽  
Pinus Taeda L

Aim of study: In this study we applied 3D point clouds generated by images obtained from an Unmanned Aerial Vehicle (UAV) to evaluate the uniformity of young forest stands.Area of study: Two commercial forest stands were selected, with two plots each. The forest species studied were Eucalyptus spp. and Pinus taeda L. and the trees had an age of 1.5 years.Material and methods: The individual trees were detected based on watershed segmentation and local maxima, using the spectral values stored in the point cloud. After the tree detection, the heights were calculated using two approaches, in the first one using the Digital Surface Model (DSM) and a Digital Terrain Model, and in the second using only the DSM. We used the UAV-derived heights to estimate an uniformity index.Main results: The trees were detected with a maximum 6% of error. However, the height was underestimated in all cases, in an average of 1 and 0.7 m for Pinus and Eucalyptus stands. We proposed to use the models built herein to estimate tree height, but the regression models did not explain the variably within the data satisfactorily. Therefore, the uniformity index calculated using the direct UAV-height values presented results close to the field inventory, reaching better results when using the second height approach (error ranging 2.8-7.8%).Research highlights: The uniformity index using the UAV-derived height from the proposed methods was close to the values obtained in field. We noted the potential for using UAV imagery in forest monitoring.

scholarly journals COMPARISON OF 3D POINT CLOUDS FROM AERIAL STEREO IMAGES AND LIDAR FOR FOREST CHANGE DETECTION

2017 ◽  
Vol XLII-3/W3 ◽  
pp. 1-5 ◽  
Author(s):  
D. Ali-Sisto ◽  
P. Packalen
Keyword(s):  
Classification Accuracy ◽  
Digital Terrain Model ◽  
Ground Level ◽  
Point Clouds ◽  
Aerial Image ◽  
Lidar Data ◽  
Forest Change ◽  
Time Points ◽  
3D Point Clouds ◽  
The Difference

This study compares performance of aerial image based point clouds (IPCs) and light detection and ranging (LiDAR) based point clouds in detection of thinnings and clear cuts in forests. IPCs are an appealing method to update forest resource data, because of their accuracy in forest height estimation and cost-efficiency of aerial image acquisition. We predicted forest changes over a period of three years by creating difference layers that displayed the difference in height or volume between the initial and subsequent time points. Both IPCs and LiDAR data were used in this process. The IPCs were constructed with the Semi-Global Matching (SGM) algorithm. Difference layers were constructed by calculating differences in fitted height or volume models or in canopy height models (CHMs) from both time points. The LiDAR-derived digital terrain model (DTM) was used to scale heights to above ground level. The study area was classified in logistic regression into the categories ClearCut, Thinning or NoChange with the values from the difference layers. We compared the predicted changes with the true changes verified in the field, and obtained at best a classification accuracy for clear cuts 93.1 % with IPCs and 91.7 % with LiDAR data. However, a classification accuracy for thinnings was only 8.0 % with IPCs. With LiDAR data 41.4 % of thinnings were detected. In conclusion, the LiDAR data proved to be more accurate method to predict the minor changes in forests than IPCs, but both methods are useful in detection of major changes.

scholarly journals A FEASIBILITY STUDY ON USE OF GENERIC MOBILE LASER SCANNING SYSTEM FOR DETECTING ASPHALT PAVEMENT CRACKS

2016 ◽  
Vol XLI-B1 ◽  
pp. 545-549 ◽  
Author(s):  
Xinqu Chen ◽  
Jonathan Li
Keyword(s):  
Laser Scanning ◽  
Crack Detection ◽  
Matched Filter ◽  
Digital Terrain Model ◽  
Point Clouds ◽  
Noise Removal ◽  
Scanning System ◽  
Pass Filter ◽  
3D Point Clouds ◽  
High Pass

This study aims to automatically detect pavement cracks on urban roads by employing the 3D point clouds acquired by a mobile laser scanning (MLS) system. Our method consists of four steps: ground point filtering, high-pass convolution, matched filtering, and noise removal. First, a voxel-based upward growing method is applied to construct Digital Terrain Model (DTM) of the road surface. Then, a high-pass filter convolutes the DTM to detect local elevation changes that may embed cracking information. Next, a two-step matched filter is applied to extract crack features. Lastly, a noise removal process is conducted to refine the results. Instead of using MLS intensity, this study takes advantages of the MLS elevation information to perform automated crack detection from large-volume, mixed-density, unstructured MLS point clouds. Four types of cracks including longitudinal, transvers, random, and alligator cracks are detected. Our results demonstrated that the proposed method works well with the RIEGL VMX-450 point clouds and can detect cracks in moderate-to-severe severity (13 - 25 mm) within a 200 m by 30 m urban road segment located in Kingston, Ontario, at one time. Due to the resolution capability, small cracks with slight severity remain unclear in the MLS point cloud.

Quantifying contemporary debris supply in a debris-covered glacier catchment using high-resolution repeat terrestrial LiDAR

2020 ◽  
Author(s):  
Rebecca Stewart ◽  
Matthew Westoby ◽  
Stuart Dunning ◽  
Francesca Pellicciotti ◽  
John Woodward
Keyword(s):  
Spatial Distribution ◽  
Large Scale ◽  
Point Clouds ◽  
Time Lapse ◽  
Detection Algorithm ◽  
Temporal Variations ◽  
Connected Components ◽  
Glacier Mass Balance ◽  
Terrestrial Lidar ◽  
3D Point Clouds

<p>Glacial debris cover is increasing at a global scale in response to increasing temperatures and negative glacier mass balance. The last decade or so has seen an abundance of research which focuses on debris-covered glacier dynamics and supraglacial processes, such as ice-cliff back wasting and the development of supraglacial ponds. However, far fewer studies have focussed on improving understanding of debris supply to these systems over short- (months-years) or long (centennial-millennial) timescales. Existing work has attempted to quantify headwall erosion by calculating the ratio of supraglacial debris flux (the product of debris thickness and supraglacial velocity) to the headwall catchment area. Whilst these studies provide estimates of headwall erosion rates over long timescales, they are unable to capture subtle (or extreme) spatial and temporal variations in debris supply that operate over shorter timescales. Capturing this variation is important because it will allow predictions of the spatial distribution and volume of debris layers on debris-covered glaciers, which in turn will increase the accuracy of ablation modelling and future melt predictions for these systems. To quantify such variability, we conducted terrestrial LiDAR surveys of potential debris slopes at Miage Glacier, Italy, between July – September 2019. We acquired > 1.8 billion 3D points per catchment survey covering an approximate slope area of 7.7 km<sup>2</sup>, which supplies debris to ~33% of the glacierised area. Sequential 3D point clouds were co-registered using iterative closest point adjustment. Vegetated surfaces were automatically detected using the CloudCompare plugin CANUPO and removed from further analysis. The M3C2 change detection algorithm was used to calculate 3D change normal to the surface plane, and a 95<sup>th</sup> percentile confidence interval was applied to eliminate non-significant change. Connected components analysis was used to identify discrete rockfall events, estimate their dimensions, explore their magnitude-frequency and quantify their spatial distribution. We find at least one large failure which developed over a period of two weeks (validated by in situ time-lapse footage) and comprised an estimated volume of around 1 x 10<sup>6</sup> m<sup>3</sup>. This particular failure occurred from a recently (<10 years) deglaciated slope, lending support to the theory that large-scale slope response to glacial erosion can be rapid.</p>

scholarly journals Optimisation of LiDAR derived terrain models for river flow modelling

2009 ◽  
Vol 13 (8) ◽  
pp. 1453-1466 ◽  
Author(s):  
G. Mandlburger ◽  
C. Hauer ◽  
B. Höfle ◽  
H. Habersack ◽  
N. Pfeifer
Keyword(s):  
River Flow ◽  
Digital Terrain Model ◽  
Expansion Ratio ◽  
Simulation Software ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Random Measurement Error ◽  
Flow Modelling ◽  
Digital Terrain ◽  
Terrain Models

Abstract. Airborne LiDAR (Light Detection And Ranging) combines cost efficiency, high degree of automation, high point density of typically 1–10 points per m2 and height accuracy of better than ±15 cm. For all these reasons LiDAR is particularly suitable for deriving precise Digital Terrain Models (DTM) as geometric basis for hydrodynamic-numerical (HN) simulations. The application of LiDAR for river flow modelling requires a series of preprocessing steps. Terrain points have to be filtered and merged with river bed data, e.g. from echo sounding. Then, a smooth Digital Terrain Model of the Watercourse (DTM-W) needs to be derived, preferably considering the random measurement error during surface interpolation. In a subsequent step, a hydraulic computation mesh has to be constructed. Hydraulic simulation software is often restricted to a limited number of nodes and elements, thus, data reduction and data conditioning of the high resolution LiDAR DTM-W becomes necessary. We will present a DTM thinning approach based on adaptive TIN refinement which allows a very effective compression of the point data (more than 95% in flood plains and up to 90% in steep areas) while preserving the most relevant topographic features (height tolerance ±20 cm). Traditional hydraulic mesh generators focus primarily on physical aspects of the computation grid like aspect ratio, expansion ratio and angle criterion. They often neglect the detailed shape of the topography as provided by LiDAR data. In contrast, our approach considers both the high geometric resolution of the LiDAR data and additional mesh quality parameters. It will be shown that the modelling results (flood extents, flow velocities, etc.) can vary remarkably by the availability of surface details. Thus, the inclusion of such geometric details in the hydraulic computation meshes is gaining importance in river flow modelling.

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