scholarly journals Automated point clouds processing for deformation monitoring

2015 ◽  
Vol 14 (2) ◽  
pp. 47-54
Author(s):  
Ján Erdélyi
Keyword(s):  
Spatial Data ◽  
Laser Scanning ◽  
Operation Mode ◽  
Weather Conditions ◽  
Point Clouds ◽  
Deformation Monitoring ◽  
Engineering Structures ◽  
Measured Object ◽  
Dynamic Function ◽  
Accuracy Of Results

<p>The weather conditions and the operation load are causing changes in the spatial position and in the shape of engineering constructions, which affects their static and dynamic function and reliability. Because these facts, geodetic measurements are integral parts of engineering structures diagnosis.</p><p>The advantage of terrestrial laser scanning (TLS) over conventional surveying methods is the efficiency of spatial data acquisition. TLS allows contactless determining the spatial coordinates of points lying on the surface on the measured object. The scan rate of current scanners (up to 1 million of points/s) allows significant reduction of time, necessary for the measurement; respectively increase the quantity of obtained information about the measured object. To increase the accuracy of results, chosen parts of the monitored construction can be approximated by single geometric entities using regression. In this case the position of measured point is calculated from tens or hundreds of scanned points.</p><p>This paper presents the possibility of deformation monitoring of engineering structures using the technology of TLS. For automated data processing was developed an application based on Matlab®, Displacement_TLS. The operation mode, the basic parts of this application and the calculation of displacements are described.</p>

scholarly journals Spatial Data Analysis for Deformation Monitoring of Bridge Structures

2020 ◽  
Vol 10 (23) ◽  
pp. 8731
Author(s):  
Ján Erdélyi ◽  
Alojz Kopáčik ◽  
Peter Kyrinovič
Keyword(s):  
Spatial Data ◽  
Laser Scanning ◽  
Deformation Monitoring ◽  
Radar Interferometry ◽  
Spatial Data Analysis ◽  
Deformation Analysis ◽  
Measuring Instruments ◽  
Engineering Structures ◽  
Essential Information ◽  
Bridge Structures

Weather conditions and different operational loads often cause changes in essential parts of engineering structures, and this affects the static and dynamic behavior and reliability of these structures. Therefore, geodetic monitoring is an integral part of the diagnosis of engineering structures and provides essential information about the current state (condition) of the structure. The development of measuring instruments enables deformation analyses of engineering structures using non-conventional surveying methods. Nowadays, one of the most effective techniques for spatial data collection is terrestrial laser scanning (TLS). TLS is frequently used for data acquisition in cases where three-dimensional (3D) data with high resolution is needed. Using suitable data processing, TLS can be used for static deformation analysis of the structure being monitored. For dynamic deformation measurements (structural health monitoring) of bridge structures, ground-based radar interferometry and accelerometers are often used for vibration mode determination using spectral analysis of frequencies. This paper describes experimental deformation monitoring of structures performed using TLS and ground-based radar interferometry. The procedure of measurement, the analysis of the acquired spatial data, and the results of deformation monitoring are explained and described.

scholarly journals Investigation of repeatability of digital surface model obtained from point clouds in a concrete arch dam for monitoring of deformations

2013 ◽  
Vol 19 (2) ◽  
pp. 268-286 ◽  
Author(s):  
Kutalmis Gumus ◽  
Halil Erkaya ◽  
Metin Soycan
Keyword(s):  
Water Reservoir ◽  
Point Clouds ◽  
Deformation Monitoring ◽  
Water Levels ◽  
Arch Dam ◽  
Surface Model ◽  
Engineering Structures ◽  
Concrete Arch Dam ◽  
Accuracy Of Measurements ◽  
Laser Scanners

Applicability of Terrestrial Laser Scanners/Scanning (TLS) in deformation measurement in dams is an active area of study. With the advance of modern technology, accuracy of measurements is much improved by developments in design of terrestrial laser scanners. Currently, this technology is used in large and complex engineering structures such as dams. Although TLS is a high cost technology, it is particularly used in monitoring of dam deformations, due to its speed in obtaining thousands of data points, ability to visualize the scanned object and its environment with high accuracy and ability to take long-range measurements. In order to determine the effect of change in water reservoir levels on body of the dam, TLS are used to take deformation measurements in different time intervals, where the water level was at maximum, minimum and medium levels. This paper provides an overview of terrestrial laser scanning technology for deformation monitoring. The concrete arch dam in Antalya Oymapinar, Turkey was used for case study. Four different scannings were performed in this dam in order to verify the replicability of TLS results on same water levels and equivalent conditions. Digital Surface Models reflecting dam surface have been created. Results obtained from surface model differences were examined using surface matching method.

scholarly journals Deformation Monitoring of Earth Fissure Hazards Using Terrestrial Laser Scanning

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1463 ◽  
Author(s):  
Yunfeng Ge ◽  
Huiming Tang ◽  
Xulong Gong ◽  
Binbin Zhao ◽  
Yi Lu ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Ground Surface ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Vertical Displacement ◽  
Deformation Monitoring ◽  
Earth Fissure ◽  
Displacement Analysis ◽  
Remote Sensing Technique ◽  
Noise Data

Deformation monitoring is a powerful tool to understand the formation mechanism of earth fissure hazards, enabling the engineering and planning efforts to be more effective. To assess the evolution characteristics of the Yangshuli earth fissure hazard more completely, terrestrial laser scanning (TLS), a remote sensing technique which is regarded as one of the most promising surveying technologies in geohazard monitoring, was employed to detect the changes to ground surfaces and buildings in small- and large-scales, respectively. Time-series of high-density point clouds were collected through 5 sequential scans from 2014 to 2017 and then pre-processing was performed to filter the noise data of point clouds. A tiny deformation was observed on both the scarp and the walls, based on the local displacement analysis. The relative height differences between the two sides of the scarp increase slowly from 0.169 m to 0.178 m, while no obvious inclining (the maximum tilt reaches just to 0.0023) happens on the two walls, based on tilt measurement. Meanwhile, global displacement analysis indicates that the overall settlement slowly increases for the ground surface, but the regions in the left side of scarp are characterized by a relatively larger vertical displacement than the right. Furthermore, the comparisons of monitoring results on the same measuring line are discussed in this study and TLS monitoring results have an acceptable consistency with the global positioning system (GPS) measurements. The case study shows that the TLS technique can provide an adequate solution in deformation monitoring of earth fissure hazards, with high effectiveness and applicability.

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.

scholarly journals RESEARCH ON COORDINATE TRANSFORMATION METHOD OF GB-SAR IMAGE SUPPORTED BY 3D LASER SCANNING TECHNOLOGY

2018 ◽  
Vol XLII-3 ◽  
pp. 1757-1763 ◽  
Author(s):  
P. Wang ◽  
C. Xing
Keyword(s):  
Coordinate System ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Estimation Method ◽  
Image Plane ◽  
Point Clouds ◽  
Transformation Method ◽  
Deformation Monitoring ◽  
Sar Image ◽  
3D Laser Scanning

In the image plane of GB-SAR, identification of deformation distribution is usually carried out by artificial interpretation. This method requires analysts to have adequate experience of radar imaging and target recognition, otherwise it can easily cause false recognition of deformation target or region. Therefore, it is very meaningful to connect two-dimensional (2D) plane coordinate system with the common three-dimensional (3D) terrain coordinate system. To improve the global accuracy and reliability of the transformation from 2D coordinates of GB-SAR images to local 3D coordinates, and overcome the limitation of traditional similarity transformation parameter estimation method, 3D laser scanning data is used to assist the transformation of GB-SAR image coordinates. A straight line fitting method for calculating horizontal angle was proposed in this paper. After projection into a consistent imaging plane, we can calculate horizontal rotation angle by using the linear characteristics of the structure in radar image and the 3D coordinate system. Aided by external elevation information by 3D laser scanning technology, we completed the matching of point clouds and pixels on the projection plane according to the geometric projection principle of GB-SAR imaging realizing the transformation calculation of GB-SAR image coordinates to local 3D coordinates. Finally, the effectiveness of the method is verified by the GB-SAR deformation monitoring experiment on the high slope of Geheyan dam.

scholarly journals THE IQMULUS URBAN SHOWCASE: AUTOMATIC TREE CLASSIFICATION AND IDENTIFICATION IN HUGE MOBILE MAPPING POINT CLOUDS

2016 ◽  
Vol XLI-B3 ◽  
pp. 301-307
Author(s):  
J. Böhm ◽  
M. Bredif ◽  
T. Gierlinger ◽  
M. Krämer ◽  
R. Lindenberg ◽  
...  
Keyword(s):  
Spatial Data ◽  
Laser Scanning ◽  
Point Clouds ◽  
Urban Trees ◽  
Automatic Identification ◽  
Mobile Mapping ◽  
3D Data ◽  
Hadoop Distributed File System ◽  
Individual Trees ◽  
Mapping Point

Current 3D data capturing as implemented on for example airborne or mobile laser scanning systems is able to efficiently sample the surface of a city by billions of unselective points during one working day. What is still difficult is to extract and visualize meaningful information hidden in these point clouds with the same efficiency. This is where the FP7 IQmulus project enters the scene. IQmulus is an interactive facility for processing and visualizing big spatial data. In this study the potential of IQmulus is demonstrated on a laser mobile mapping point cloud of 1 billion points sampling ~ 10 km of street environment in Toulouse, France. After the data is uploaded to the IQmulus Hadoop Distributed File System, a workflow is defined by the user consisting of retiling the data followed by a PCA driven local dimensionality analysis, which runs efficiently on the IQmulus cloud facility using a Spark implementation. Points scattering in 3 directions are clustered in the tree class, and are separated next into individual trees. Five hours of processing at the 12 node computing cluster results in the automatic identification of 4000+ urban trees. Visualization of the results in the IQmulus fat client helps users to appreciate the results, and developers to identify remaining flaws in the processing workflow.

scholarly journals THE COMBINATION OF THE IMAGE AND RANGE-BASED 3D ACQUISITION IN ARCHAEOLOGICAL AND ARCHITECTURAL RESEARCH IN THE ROYAL CASTLE IN WARSAW

2019 ◽  
Vol XLII-2/W15 ◽  
pp. 177-184
Author(s):  
A. Bocheńska ◽  
J. Markiewicz ◽  
S. Łapiński
Keyword(s):  
Laser Scanning ◽  
Retaining Wall ◽  
Weather Conditions ◽  
Point Clouds ◽  
Sensor Data ◽  
Data Sets ◽  
Multi Temporal ◽  
Close Range ◽  
3D Acquisition ◽  
Architectural Research

<p><strong>Abstract.</strong> The paper presents archaeological and architectural research in the Royal Castle in Warsaw where a combination of image- and range-based 3D acquisition was applied. The area examined included excavations situated inside the Tower and near its outer western wall. The work was carried out at various periods and in different weather conditions. As part of the measurements, laser scanning was performed (with a Z+F 5006h scanner) and a series of close-range images were taken. It was important to integrate the data acquired to create a comprehensive documentation of archaeological excavations. When data was acquired from TLS together with photogrammetric data (in different measurement periods), the points' displacements were controlled and analysed. The process of orienting and processing the terrestrial images included photographs taken during the inventory of the tower (Canon 5D Mark II) and photographs provided by the Castle's employees (Canon PowerShot G5 X). Agisoft PhotoScan software was used to orient and process the terrestrial images, and LupoScan for the TLS data. In order to integrate the TLS data and the clouds of points from the photographs from the various stages, they were processed into a raster form; our own software (based on the OpenCV library and the Structure-from-Motion method) and LupoScan software were used to interconnect the multi-temporal and multi-sensor data sets. As a result of processing photographs and TLS data, point clouds in an external reference system were obtained. This data was then used to study the thickness of the walls of the Justice Court Tower, to analyse the course of the retaining wall, and to generate the orthoimages necessary for chronological analysis.</p>

scholarly journals Full 4D Change Analysis of Topographic Point Cloud Time Series using Kalman Filtering

2022 ◽  
Author(s):  
Lukas Winiwarter ◽  
Katharina Anders ◽  
Daniel Schröder ◽  
Bernhard Höfle
Keyword(s):  
Time Series ◽  
Kalman Filter ◽  
Spatial Data ◽  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
High Mountain ◽  
Change Analysis ◽  
Area Of Interest ◽  
Rainy Weather

Abstract. 4D topographic point cloud data contain information on surface change processes and their spatial and temporal characteristics, such as the duration, location, and extent of mass movements, e.g., rockfalls or debris flows. To automatically extract and analyse change and activity patterns from this data, methods considering the spatial and temporal properties are required. The commonly used M3C2 point cloud distance reduces uncertainty through spatial averaging for bitemporal analysis. To extend this concept into the full 4D domain, we use a Kalman filter for point cloud change analysis. The filter incorporates M3C2 distances together with uncertainties obtained through error propagation as Bayesian priors in a dynamic model. The Kalman filter yields a smoothed estimate of the change time series for each spatial location, again associated with an uncertainty. Through the temporal smoothing, the Kalman filter uncertainty is, in general, lower than the individual bitemporal uncertainties, which therefore allows detection of more change as significant. In our example time series of bi-hourly terrestrial laser scanning point clouds of around 6 days (71 epochs) showcasing a rockfall-affected high-mountain slope in Tyrol, Austria, we are able to almost double the number of points where change is deemed significant (from 14.9 % to 28.6 % of the area of interest). Since the Kalman filter allows interpolation and, under certain constraints, also extrapolation of the time series, the estimated change values can be temporally resampled. This can be critical for subsequent analyses that are unable to deal with missing data, as may be caused by, e.g., foggy or rainy weather conditions. We demonstrate two different clustering approaches, transforming the 4D data into 2D map visualisations that can be easily interpreted by analysts. By comparison to two state-of-the-art 4D point cloud change methods, we highlight the main advantage of our method to be the extraction of a smoothed best estimate time series for change at each location. A main disadvantage of not being able to detect spatially overlapping change objects in a single pass remains. In conclusion, the consideration of combined temporal and spatial data enables a notable reduction in the associated uncertainty of the quantified change value for each point in space and time, in turn allowing the extraction of more information from the 4D point cloud dataset.

scholarly journals EVALUATING THE POSSIBILITY OF TREE SPECIES CLASSIFICATION WITH DUAL-WAVELENGTH ALS DATA

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1097-1103
Author(s):  
M. Pilarska ◽  
W. Ostrowski
Keyword(s):  
Spatial Data ◽  
Tree Species ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Point Clouds ◽  
Airborne Laser Scanning ◽  
Tree Canopy ◽  
Species Classification ◽  
Airborne Laser ◽  
Simultaneous Acquisition

<p><strong>Abstract.</strong> Airborne laser scanning (ALS) plays an important role in spatial data acquisition. One of the advantages of this technique is laser beam penetration through vegetation, which makes it possible to not only obtain data on the tree canopy but also within and under the canopy. In recent years, multi-wavelength airborne laser scanning has been developed. This technique consists of simultaneous acquisition of point clouds in more than one band. The aim of this experiment was to examine and assess the possibilities of tree segmentation and species classification in an urban area. In this experiment, point clouds registered in two wavelengths (532 and 1064&amp;thinsp;nm) were used for tree segmentation and species classification. The data were acquired with a Riegl VQ-1560i-DW laser scanner over Elblag, Poland, during August 2018. Tree species collected by a botanist team within terrain measurements were used as a reference in the classification process. Within the experiment segmentation and classification process were performed. Regarding the segmentation, TerraScan software and Li et al.’s algorithm, implemented in LidR package were used. Results from both methods are clearly over-segmented in comparison to the manual segments. In Terrasolid segmentation, single reference segments are over-segmented in 28% of cases, whereas, for LidR, over-segmentation occurred in 73% of the segments. According the classification results, Thuja, Salix and Betula were the species, for which the highest classification accuracy was achieved.</p>

scholarly journals Three-Dimensional Unique-Identifier-Based Automated Georeferencing and Coregistration of Point Clouds in Underground Mines

2021 ◽  
Vol 13 (16) ◽  
pp. 3145
Author(s):  
Sarvesh Kumar Singh ◽  
Bikram Pratap Banerjee ◽  
Simit Raval
Keyword(s):  
Convergence Rate ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Point Clouds ◽  
Deformation Monitoring ◽  
Reference Points ◽  
Underground Mines ◽  
Sensor Calibration ◽  
3D Registration ◽  
Rock Falls

Spatially referenced and geometrically accurate laser scans are essential for mapping and monitoring applications in underground mines to ensure safe and smooth operation. However, obtaining an absolute 3D map in an underground mine environment is challenging using laser scanning due to the unavailability of global navigation satellite system (GNSS) signals. Consequently, applications that require georeferenced point cloud or coregistered multitemporal point clouds such as detecting changes, monitoring deformations, tracking mine logistics, measuring roadway convergence rate and evaluating construction performance become challenging. Current mapping practices largely include a manual selection of discernable reference points in laser scans for georeferencing and coregistration which is often time-consuming, arduous and error-prone. Moreover, challenges in obtaining a sensor positioning framework, the presence of structurally symmetric layouts and highly repetitive features (such as roof bolts) makes the multitemporal scans difficult to georeference and coregister. This study aims at overcoming these practical challenges through development of three-dimensional unique identifiers (3DUIDs) and a 3D registration (3DReG) workflow. Field testing of the developed approach in an underground coal mine has been found effective with an accuracy of 1.76 m in georeferencing and 0.16 m in coregistration for a scan length of 850 m. Additionally, automatic extraction of mine roadway profile has been demonstrated using 3DUID which is often a compliant and operational requirement for mitigating roadway related hazards that includes roadway convergence rate, roof/rock falls, floor heaves and vehicle clearance for collision avoidance. Potential applications of 3DUID include roadway profile extraction, guided automation, sensor calibration, reference targets for a routine survey and deformation monitoring.

Sign in / Sign up

Export Citation Format

Share Document