Influence of the simplified stochastic model of TLS measurements on geometry-based deformation analysis

2019 ◽  
Vol 13 (3) ◽  
pp. 199-214 ◽  
Author(s):  
Xin Zhao ◽  
Gaël Kermarrec ◽  
Boris Kargoll ◽  
Hamza Alkhatib ◽  
Ingo Neumann
Keyword(s):  
Stochastic Model ◽  
Stochastic Models ◽  
Null Hypothesis ◽  
Point Clouds ◽  
Deformation Monitoring ◽  
B Splines ◽  
3D Point Clouds ◽  
Relative Standard ◽  
Deformation Area ◽  
The Impact

Abstract Terrestrial laser scanners (TLS) are powerful instruments that can be employed for deformation monitoring due to their high precision and spatial resolution in capturing 3D point clouds. Deformation detections from scatter point clouds can be based on different comparison methods, among which the geometry-based method is one of the most popular. Compared with approximating surfaces with predetermined geometric primitives, such as plane or sphere, the B-splines surface approximation offers a great flexibility and can be used to fit nearly every object scanned with TLS. However, a variance-covariance matrix (VCM) of the observations involved in approximating the scattered points to B-spline surfaces impact the results of a congruency test, which is the uniformly most powerful invariant (UMPI) test for discriminating between the null hypothesis of zero deformation and its alternative hypotheses. Consequently, simplified stochastic models may weaken the UMPI property. Based on Monte Carlo simulations, the impact of the heteroscedasticity and mathematical correlations often neglected in B-splines approximation are investigated. These correlations are specific in approximating TLS measurements when the raw measurements are transformed into Cartesian coordinates. The rates of rejecting the null hypothesis in a congruency test is employed to reflect the impact of unspecified VCMs on the power of the congruency test. The rejection rates are not sensitive to the simplification of the stochastic models, in the larger deformation area with higher point accuracy, while they are obviously influenced in the smaller deformation area with unfavourable geometries, i. e. larger uncertainties. A threshold ratio of estimated differences to the relative standard deviation highlights whereas the results of congruency test are reliable when using simplified VCMs. It is concluded that the simplification of the stochastic model has a significant impact on the power of the congruency test, especially in the smaller deformation area with larger uncertainties.

scholarly journals How Significant Are Differences Obtained by Neglecting Correlations When Testing for Deformation: A Real Case Study Using Bootstrapping with Terrestrial Laser Scanner Observations Approximated by B-Spline Surfaces

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3640 ◽  
Author(s):  
Kermarrec ◽  
Paffenholz ◽  
Alkhatib
Keyword(s):  
Stochastic Model ◽  
Statistical Tests ◽  
Point Clouds ◽  
Least Square ◽  
Test Statistics ◽  
B Spline ◽  
3D Point Clouds ◽  
Spline Surfaces ◽  
Propagation Law ◽  
The Impact

B-spline surfaces possess attractive properties such as a high degree of continuity or the local support of their basis functions. One of the major applications of B-spline surfaces in engineering geodesy is the least-square (LS) fitting of surfaces from, e.g., 3D point clouds obtained from terrestrial laser scanners (TLS). Such mathematical approximations allow one to test rigorously with a given significance level the deformation magnitude between point clouds taken at different epochs. Indeed, statistical tests cannot be applied when point clouds are processed in commonly used software such as CloudCompare, which restrict the analysis of deformation to simple deformation maps based on distance computation. For a trustworthy test decision and a resulting risk management, the stochastic model of the underlying observations needs, however, to be optimally specified. Since B-spline surface approximations necessitate Cartesian coordinates of the TLS observations, the diagonal variance covariance matrix (VCM) of the raw TLS measurements has to be transformed by means of the error propagation law. Unfortunately, this procedure induces mathematical correlations, which can strongly affect the chosen test statistics to analyse deformation, if neglected. This may lead potentially to rejecting wrongly the null hypothesis of no-deformation, with risky and expensive consequences. In this contribution, we propose to investigate the impact of mathematical correlations on test statistics, using real TLS observations from a bridge under load. As besides TLS, a highly precise laser tracker (LT) was used, the significance of the difference of the test statistics when the stochastic model is misspecified can be assessed. However, the underlying test distribution is hardly tractable so that only an adapted bootstrapping allows the computation of trustworthy p-values. Consecutively, the extent to which heteroscedasticity and mathematical correlations can be neglected or simplified without impacting the test decision is shown in a rigorous way, paving the way for a simplification based on the intensity model.

scholarly journals GEOMETRIC FEATURES ANALYSIS FOR THE CLASSIFICATION OF CULTURAL HERITAGE POINT CLOUDS

2019 ◽  
Vol XLII-2/W15 ◽  
pp. 541-548 ◽  
Author(s):  
E. Grilli ◽  
E. M. Farella ◽  
A. Torresani ◽  
F. Remondino
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cultural Heritage ◽  
Point Clouds ◽  
Geometric Features ◽  
3D Point Clouds ◽  
3D Data ◽  
3D Documentation ◽  
The Impact

<p><strong>Abstract.</strong> In the last years, the application of artificial intelligence (Machine Learning and Deep Learning methods) for the classification of 3D point clouds has become an important task in modern 3D documentation and modelling applications. The identification of proper geometric and radiometric features becomes fundamental to classify 2D/3D data correctly. While many studies have been conducted in the geospatial field, the cultural heritage sector is still partly unexplored. In this paper we analyse the efficacy of the geometric covariance features as a support for the classification of Cultural Heritage point clouds. To analyse the impact of the different features calculated on spherical neighbourhoods at various radius sizes, we present results obtained on four different heritage case studies using different features configurations.</p>

Robust external calibration of terrestrial laser scanner and digital camera for structural monitoring

2019 ◽  
Vol 13 (2) ◽  
pp. 105-134 ◽  
Author(s):  
Mohammad Omidalizarandi ◽  
Boris Kargoll ◽  
Jens-André Paffenholz ◽  
Ingo Neumann
Keyword(s):  
Em Algorithm ◽  
Laser Scanner ◽  
Digital Camera ◽  
Point Clouds ◽  
Deformation Monitoring ◽  
Deformation Analysis ◽  
Terrestrial Laser Scanner ◽  
External Calibration ◽  
3D Point Clouds ◽  
Calibration Parameters

Abstract In the last two decades, the integration of a terrestrial laser scanner (TLS) and digital photogrammetry, besides other sensors integration, has received considerable attention for deformation monitoring of natural or man-made structures. Typically, a TLS is used for an area-based deformation analysis. A high-resolution digital camera may be attached on top of the TLS to increase the accuracy and completeness of deformation analysis by optimally combining points or line features extracted both from three-dimensional (3D) point clouds and captured images at different epochs of time. For this purpose, the external calibration parameters between the TLS and digital camera needs to be determined precisely. The camera calibration and internal TLS calibration are commonly carried out in advance in the laboratory environments. The focus of this research is to highly accurately and robustly estimate the external calibration parameters between the fused sensors using signalised target points. The observables are the image measurements, the 3D point clouds, and the horizontal angle reading of a TLS. In addition, laser tracker observations are used for the purpose of validation. The functional models are determined based on the space resection in photogrammetry using the collinearity condition equations, the 3D Helmert transformation and the constraint equation, which are solved in a rigorous bundle adjustment procedure. Three different adjustment procedures are developed and implemented: (1) an expectation maximization (EM) algorithm to solve a Gauss-Helmert model (GHM) with grouped t-distributed random deviations, (2) a novel EM algorithm to solve a corresponding quasi-Gauss-Markov model (qGMM) with t-distributed pseudo-misclosures, and (3) a classical least-squares procedure to solve the GHM with variance components and outlier removal. The comparison of the results demonstrates the precise, reliable, accurate and robust estimation of the parameters in particular by the second and third procedures in comparison to the first one. In addition, the results show that the second procedure is computationally more efficient than the other two.

scholarly journals Estimating Control Points for B-Spline Surfaces Using Fully Populated Synthetic Variance–Covariance Matrices for TLS Point Clouds

2021 ◽  
Vol 13 (16) ◽  
pp. 3124
Author(s):  
Jakob Raschhofer ◽  
Gabriel Kerekes ◽  
Corinna Harmening ◽  
Hans Neuner ◽  
Volker Schwieger
Keyword(s):  
Laser Scanning ◽  
Point Clouds ◽  
Covariance Matrices ◽  
Geometric Modelling ◽  
Deformation Analysis ◽  
Control Points ◽  
B Splines ◽  
Spline Surfaces ◽  
Standard Deviations ◽  
The Impact

A flexible approach for geometric modelling of point clouds obtained from Terrestrial Laser Scanning (TLS) is by means of B-splines. These functions have gained some popularity in the engineering geodesy as they provide a suitable basis for a spatially continuous and parametric deformation analysis. In the predominant studies on geometric modelling of point clouds by B-splines, uncorrelated and equally weighted measurements are assumed. Trying to overcome this, the elementary errors theory is applied for establishing fully populated covariance matrices of TLS observations that consider correlations in the observed point clouds. In this article, a systematic approach for establishing realistic synthetic variance–covariance matrices (SVCMs) is presented and afterward used to model TLS point clouds by B-splines. Additionally, three criteria are selected to analyze the impact of different SVCMs on the functional and stochastic components of the estimation results. Plausible levels for variances and covariances are obtained using a test specimen of several dm—dimension. It is used to identify the most dominant elementary errors under laboratory conditions. Starting values for the variance level are obtained from a TLS calibration. The impact of SVCMs with different structures and different numeric values are comparatively investigated. Main findings of the paper are that for the analyzed object size and distances, the structure of the covariance matrix does not significantly affect the location of the estimated surface control points, but their precision in terms of the corresponding standard deviations. Regarding the latter, properly setting the main diagonal terms of the SVCM is of superordinate importance compared to setting the off-diagonal ones. The investigation of some individual errors revealed that the influence of their standard deviation on the precision of the estimated parameters is primarily dependent on the scanning distance. When the distance stays the same, one-sided influences on the precision of the estimated control points can be observed with an increase in the standard deviations.

scholarly journals Deformation monitoring using laser scanned point clouds and BIM

2018 ◽  
Vol 245 ◽  
pp. 01002 ◽  
Author(s):  
Vladimir Badenko ◽  
Dmitry Volgin ◽  
Sergey Lytkin
Keyword(s):  
Laser Scanning ◽  
Point Clouds ◽  
Deformation Monitoring ◽  
Future Research ◽  
Cloud Processing ◽  
3D Point Clouds ◽  
Point Cloud Processing ◽  
Informational Model ◽  
Technology Gaps

Laser scanning is an essential method for monitoring of the operation of buildings or structures. It involves creating as-is BIM from point clouds obtained from laser scanning. In this article we present our workflow for the generation of information model from 3D point clouds of concrete tetrapod blocks on navigable structure C-1. Point cloud processing method for making informational model for long term monitoring is described. As a result of the research BIM model with each tetrapod was created for deformational monitoring in the comparison with next year model. Finally, we identify and discuss technology gaps that need to be addressed in future research.

scholarly journals MORPHOLOGICAL CHANGES ALONG A DIKE LANDSIDE SLOPE SAMPLED BY 4D HIGH RESOLUTION TERRESTRIAL LASER SCANNING

2016 ◽  
Vol XLI-B3 ◽  
pp. 227-232 ◽  
Author(s):  
Mónica Herrero-Huertaa ◽  
Roderik Lindenbergh ◽  
Luc Ponsioen ◽  
Myron van Damme
Keyword(s):  
High Resolution ◽  
Laser Scanning ◽  
Morphological Changes ◽  
Terrestrial Laser Scanning ◽  
Emergency Situation ◽  
Point Clouds ◽  
Data Sampling ◽  
Wave Overtopping ◽  
3D Point Clouds ◽  
The Impact

Emergence of light detection and ranging (LiDAR) technology provides new tools for geomorphologic studies improving spatial and temporal resolution of data sampling hydrogeological instability phenomena. Specifically, terrestrial laser scanning (TLS) collects high resolution 3D point clouds allowing more accurate monitoring of erosion rates and processes, and thus, quantify the geomorphologic change on vertical landforms like dike landside slopes. Even so, TLS captures observations rapidly and automatically but unselectively. &lt;br&gt;&lt;br&gt; In this research, we demonstrate the potential of TLS for morphological change detection, profile creation and time series analysis in an emergency simulation for characterizing and monitoring slope movements in a dike. The experiment was performed near Schellebelle (Belgium) in November 2015, using a Leica Scan Station C10. Wave overtopping and overflow over a dike were simulated whereby the loading conditions were incrementally increased and 14 successful scans were performed. The aim of the present study is to analyse short-term morphological dynamic processes and the spatial distribution of erosion and deposition areas along a dike landside slope. As a result, we are able to quantify the eroded material coming from the impact on the terrain induced by wave overtopping which caused the dike failure in a few minutes in normal storm scenarios (Q = 25 l/s/m) as 1.24 m&lt;sup&gt;3&lt;/sup&gt;. As this shows that the amount of erosion is measurable using close range techniques; the amount and rate of erosion could be monitored to predict dike collapse in emergency situation. &lt;br&gt;&lt;br&gt; The results confirm the feasibility of the proposed methodology, providing scalability to a comprehensive analysis over a large extension of a dike (tens of meters).

Canal Reach Deformation Monitoring Technology Based on Terrestrial LIDAR Scanning Technology

2011 ◽  
Vol 383-390 ◽  
pp. 4175-4179
Author(s):  
Jun Liang ◽  
Xian Quan Han ◽  
Bo Hu ◽  
Yong Tan
Keyword(s):  
Water Immersion ◽  
Point Clouds ◽  
Immersion Test ◽  
Test Point ◽  
Water Transfer ◽  
Deformation Monitoring ◽  
Concrete Lining ◽  
Terrestrial Lidar ◽  
North Water ◽  
Deformation Area

Canal reach soil of the south to north water transfer project has a certain expansibility. Concrete lining plates is puring on the soil. After about half a year of water immersion test, a certain plastic deformation has happened on the lining plates because of the expansion in the soil. In order to monitoring the whole lining plates deformation pre and post water immersion test, point clouds is collected by Terrestrial LIDAR and total station. After constructing of triangular mesh and endowing the specific color, the two group of mesh can be overlapping displayed. Deformation area can be found easily by color distortion on superposition chart. And then deformation tendency is concluded.

scholarly journals Identification of stable areas in unreferenced laser scans for automated geomorphometric monitoring

2018 ◽  
Vol 6 (2) ◽  
pp. 303-317 ◽  
Author(s):  
Daniel Wujanz ◽  
Michael Avian ◽  
Daniel Krueger ◽  
Frank Neitzel
Keyword(s):  
Point Cloud ◽  
Region Of Interest ◽  
Point Clouds ◽  
Deformation Monitoring ◽  
Cloud Data ◽  
Laser Scanners ◽  
Research Questions ◽  
Iterative Closest Point Algorithm ◽  
The Impact

Abstract. Current research questions in the field of geomorphology focus on the impact of climate change on several processes subsequently causing natural hazards. Geodetic deformation measurements are a suitable tool to document such geomorphic mechanisms, e.g. by capturing a region of interest with terrestrial laser scanners which results in a so-called 3-D point cloud. The main problem in deformation monitoring is the transformation of 3-D point clouds captured at different points in time (epochs) into a stable reference coordinate system. In this contribution, a surface-based registration methodology is applied, termed the iterative closest proximity algorithm (ICProx), that solely uses point cloud data as input, similar to the iterative closest point algorithm (ICP). The aim of this study is to automatically classify deformations that occurred at a rock glacier and an ice glacier, as well as in a rockfall area. For every case study, two epochs were processed, while the datasets notably differ in terms of geometric characteristics, distribution and magnitude of deformation. In summary, the ICProx algorithm's classification accuracy is 70 % on average in comparison to reference data.

scholarly journals Accuracy Assessment of Cultural Heritage Models Extracting 3D Point Cloud Geometric Features with RPAS SfM-MVS and TLS Techniques

Drones ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 145
Author(s):  
Alessandra Capolupo
Keyword(s):  
Cultural Heritage ◽  
Point Cloud ◽  
Accuracy Assessment ◽  
Laser Scanner ◽  
Point Clouds ◽  
Geometric Features ◽  
3D Point Cloud ◽  
3D Point Clouds ◽  
Research Activities ◽  
The Impact

A proper classification of 3D point clouds allows fully exploiting data potentiality in assessing and preserving cultural heritage. Point cloud classification workflow is commonly based on the selection and extraction of respective geometric features. Although several research activities have investigated the impact of geometric features on classification outcomes accuracy, only a few works focused on their accuracy and reliability. This paper investigates the accuracy of 3D point cloud geometric features through a statistical analysis based on their corresponding eigenvalues and covariance with the aim of exploiting their effectiveness for cultural heritage classification. The proposed approach was separately applied on two high-quality 3D point clouds of the All Saints’ Monastery of Cuti (Bari, Southern Italy), generated using two competing survey techniques: Remotely Piloted Aircraft System (RPAS) Structure from Motion (SfM) and Multi View Stereo (MVS) techniques and Terrestrial Laser Scanner (TLS). Point cloud compatibility was guaranteed through re-alignment and co-registration of data. The geometric features accuracy obtained by adopting the RPAS digital photogrammetric and TLS models was consequently analyzed and presented. Lastly, a discussion on convergences and divergences of these results is also provided.

scholarly journals UNCERTAINTY PROPAGATION FOR TERRESTRIAL MOBILE LASER SCANNER

2016 ◽  
Vol XLI-B3 ◽  
pp. 331-335 ◽  
Author(s):  
c Mezian ◽  
Bruno Vallet ◽  
Bahman Soheilian ◽  
Nicolas Paparoditis
Keyword(s):  
Uncertainty Analysis ◽  
Covariance Matrix ◽  
Uncertainty Propagation ◽  
Laser Scanner ◽  
Point Clouds ◽  
3D Point Clouds ◽  
Laser Scanners ◽  
Propagation Technique ◽  
The Impact

Laser scanners are used more and more in mobile mapping systems. They provide 3D point clouds that are used for object reconstruction and registration of the system. For both of those applications, uncertainty analysis of 3D points is of great interest but rarely investigated in the literature. In this paper we present a complete pipeline that takes into account all the sources of uncertainties and allows to compute a covariance matrix per 3D point. The sources of uncertainties are laser scanner, calibration of the scanner in relation to the vehicle and direct georeferencing system. We suppose that all the uncertainties follow the Gaussian law. The variances of the laser scanner measurements (two angles and one distance) are usually evaluated by the constructors. This is also the case for integrated direct georeferencing devices. Residuals of the calibration process were used to estimate the covariance matrix of the 6D transformation between scanner laser and the vehicle system. Knowing the variances of all sources of uncertainties, we applied uncertainty propagation technique to compute the variance-covariance matrix of every obtained 3D point. Such an uncertainty analysis enables to estimate the impact of different laser scanners and georeferencing devices on the quality of obtained 3D points. The obtained uncertainty values were illustrated using error ellipsoids on different datasets.

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