Identification of Rock Slope Discontinuity Sets from Laser Scanner and Photogrammetric Point Clouds: A Comparative Analysis

Stabilization projects of rock masses cannot be performed without a proper geomechanical characterization. The classical approaches, due to logistic issues, typically are not able to cover extensively the areas under study. Geo-structural analysis on point cloud from terrestrial laser scanning and photogrammetry from unmanned aerial vehicles are valid tools for analysis of discontinuity systems. Such methodologies provide reliable data even in complex environmental settings (active cliffs) or at inaccessible sites (excavation fronts in tunnels), offering advantages in terms of both safety of the operators and economic and time issues. We present the implementation of these techniques at a tuff cliff over the Santa Caterina beach (Campania) and at the main entrance of Castellana Caves (Apulia). In the first case study, we also perform an integration of the two techniques. Both sites are of significant tourist and economic value, and present instability conditions common to wide areas of southern Italy: namely, retrogressive evolution of active cliffs along the coast, and instability at the rims of natural and/or artificial sinkholes. The results show the reliability of the data obtained through semi-automatic methods to extract the discontinuity sets from the point clouds, and their agreement with data collected in the field through classical approaches. Advantages and drawbacks of the techniques are illustrated and discussed.

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Rock slope discontinuity extraction from 3D point clouds: Application to an open pit limestone quarry

Warta Geologi ◽

10.7186/wg462202011 ◽

2020 ◽

Vol 46 (2) ◽

pp. 107-112

Author(s):

Syaran Suri ◽

◽

Aisyah Shahirah Juhari ◽

Solihin Yaacob ◽

Nur Emilia Qistina Anua ◽

...

Keyword(s):

Rock Slope ◽

Point Clouds ◽

Open Pit ◽

Limestone Quarry ◽

3D Point Clouds ◽

Slope Discontinuity

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Fitting Terrestrial Laser Scanner Point Clouds with T-Splines: Local Refinement Strategy for Rigid Body Motion

Remote Sensing ◽

10.3390/rs13132494 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2494

Author(s):

Gaël Kermarrec ◽

Niklas Schild ◽

Jan Hartmann

Keyword(s):

Point Cloud ◽

Goodness Of Fit ◽

Laser Scanner ◽

Point Clouds ◽

Statistical Testing ◽

Computational Time ◽

Terrestrial Laser Scanner ◽

Reference Surface ◽

Local Refinement ◽

Surface Approximation

T-splines have recently been introduced to represent objects of arbitrary shapes using a smaller number of control points than the conventional non-uniform rational B-splines (NURBS) or B-spline representatizons in computer-aided design, computer graphics and reverse engineering. They are flexible in representing complex surface shapes and economic in terms of parameters as they enable local refinement. This property is a great advantage when dense, scattered and noisy point clouds are approximated using least squares fitting, such as those from a terrestrial laser scanner (TLS). Unfortunately, when it comes to assessing the goodness of fit of the surface approximation with a real dataset, only a noisy point cloud can be approximated: (i) a low root mean squared error (RMSE) can be linked with an overfitting, i.e., a fitting of the noise, and should be correspondingly avoided, and (ii) a high RMSE is synonymous with a lack of details. To address the challenge of judging the approximation, the reference surface should be entirely known: this can be solved by printing a mathematically defined T-splines reference surface in three dimensions (3D) and modeling the artefacts induced by the 3D printing. Once scanned under different configurations, it is possible to assess the goodness of fit of the approximation for a noisy and potentially gappy point cloud and compare it with the traditional but less flexible NURBS. The advantages of T-splines local refinement open the door for further applications within a geodetic context such as rigorous statistical testing of deformation. Two different scans from a slightly deformed object were approximated; we found that more than 40% of the computational time could be saved without affecting the goodness of fit of the surface approximation by using the same mesh for the two epochs.

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Using Least-Squares Residuals to Assess the Stochasticity of Measurements—Example: Terrestrial Laser Scanner and Surface Modeling

Engineering Proceedings ◽

10.3390/engproc2021005059 ◽

2021 ◽

Vol 5 (1) ◽

pp. 59

Author(s):

Gaël Kermarrec ◽

Niklas Schild ◽

Jan Hartmann

Keyword(s):

Least Squares ◽

Laser Scanner ◽

Real Data ◽

Point Clouds ◽

Statistical Testing ◽

Normal Vector ◽

Engineering Structures ◽

3D Point Clouds ◽

Least Squares Adjustment ◽

Correlation Parameters

Terrestrial laser scanners (TLS) capture a large number of 3D points rapidly, with high precision and spatial resolution. These scanners are used for applications as diverse as modeling architectural or engineering structures, but also high-resolution mapping of terrain. The noise of the observations cannot be assumed to be strictly corresponding to white noise: besides being heteroscedastic, correlations between observations are likely to appear due to the high scanning rate. Unfortunately, if the variance can sometimes be modeled based on physical or empirical considerations, the latter are more often neglected. Trustworthy knowledge is, however, mandatory to avoid the overestimation of the precision of the point cloud and, potentially, the non-detection of deformation between scans recorded at different epochs using statistical testing strategies. The TLS point clouds can be approximated with parametric surfaces, such as planes, using the Gauss–Helmert model, or the newly introduced T-splines surfaces. In both cases, the goal is to minimize the squared distance between the observations and the approximated surfaces in order to estimate parameters, such as normal vector or control points. In this contribution, we will show how the residuals of the surface approximation can be used to derive the correlation structure of the noise of the observations. We will estimate the correlation parameters using the Whittle maximum likelihood and use comparable simulations and real data to validate our methodology. Using the least-squares adjustment as a “filter of the geometry” paves the way for the determination of a correlation model for many sensors recording 3D point clouds.

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LiDAR-Based Glass Detection for Improved Occupancy Grid Mapping

Sensors ◽

10.3390/s21072263 ◽

2021 ◽

Vol 21 (7) ◽

pp. 2263

Author(s):

Haileleol Tibebu ◽

Jamie Roche ◽

Varuna De Silva ◽

Ahmet Kondoz

Keyword(s):

Laser Scanner ◽

Point Clouds ◽

Light Detection And Ranging ◽

Test Results ◽

New Approach ◽

Occupancy Grid ◽

Laser Scanners ◽

Novel Method ◽

Occupancy Grid Mapping ◽

Cartesian Coordinate

Creating an accurate awareness of the environment using laser scanners is a major challenge in robotics and auto industries. LiDAR (light detection and ranging) is a powerful laser scanner that provides a detailed map of the environment. However, efficient and accurate mapping of the environment is yet to be obtained, as most modern environments contain glass, which is invisible to LiDAR. In this paper, a method to effectively detect and localise glass using LiDAR sensors is proposed. This new approach is based on the variation of range measurements between neighbouring point clouds, using a two-step filter. The first filter examines the change in the standard deviation of neighbouring clouds. The second filter uses a change in distance and intensity between neighbouring pules to refine the results from the first filter and estimate the glass profile width before updating the cartesian coordinate and range measurement by the instrument. Test results demonstrate the detection and localisation of glass and the elimination of errors caused by glass in occupancy grid maps. This novel method detects frameless glass from a long range and does not depend on intensity peak with an accuracy of 96.2%.

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Towards footwear manufacturing 4.0: shoe sole robotic grasping in assembling operations

The International Journal of Advanced Manufacturing Technology ◽

10.1007/s00170-021-06697-0 ◽

2021 ◽

Author(s):

Guillermo Oliver ◽

Pablo Gil ◽

Jose F. Gomez ◽

Fernando Torres

Keyword(s):

Industry 4.0 ◽

Laser Scanner ◽

Point Clouds ◽

Average Score ◽

Low Density ◽

Contour Extraction ◽

Shoe Industry ◽

Simulated Environment ◽

Dense Point ◽

Different Shapes

AbstractIn this paper, we present a robotic workcell for task automation in footwear manufacturing such as sole digitization, glue dispensing, and sole manipulation from different places within the factory plant. We aim to make progress towards shoe industry 4.0. To achieve it, we have implemented a novel sole grasping method, compatible with soles of different shapes, sizes, and materials, by exploiting the particular characteristics of these objects. Our proposal is able to work well with low density point clouds from a single RGBD camera and also with dense point clouds obtained from a laser scanner digitizer. The method computes antipodal grasping points from visual data in both cases and it does not require a previous recognition of sole. It relies on sole contour extraction using concave hulls and measuring the curvature on contour areas. Our method was tested both in a simulated environment and in real conditions of manufacturing at INESCOP facilities, processing 20 soles with different sizes and characteristics. Grasps were performed in two different configurations, obtaining an average score of 97.5% of successful real grasps for soles without heel made with materials of low or medium flexibility. In both cases, the grasping method was tested without carrying out tactile control throughout the task.

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Comparison of Depth Camera and Terrestrial Laser Scanner in Monitoring Structural Deflections

Sensors ◽

10.3390/s21010201 ◽

2020 ◽

Vol 21 (1) ◽

pp. 201

Author(s):

Michael Bekele Maru ◽

Donghwan Lee ◽

Kassahun Demissie Tola ◽

Seunghee Park

Keyword(s):

Optical Sensors ◽

Point Cloud ◽

Three Dimensional ◽

Laser Scanner ◽

Point Clouds ◽

Depth Camera ◽

Terrestrial Laser Scanner ◽

Cloud Data ◽

3D Point Clouds ◽

3D Information

Modeling a structure in the virtual world using three-dimensional (3D) information enhances our understanding, while also aiding in the visualization, of how a structure reacts to any disturbance. Generally, 3D point clouds are used for determining structural behavioral changes. Light detection and ranging (LiDAR) is one of the crucial ways by which a 3D point cloud dataset can be generated. Additionally, 3D cameras are commonly used to develop a point cloud containing many points on the external surface of an object around it. The main objective of this study was to compare the performance of optical sensors, namely a depth camera (DC) and terrestrial laser scanner (TLS) in estimating structural deflection. We also utilized bilateral filtering techniques, which are commonly used in image processing, on the point cloud data for enhancing their accuracy and increasing the application prospects of these sensors in structure health monitoring. The results from these sensors were validated by comparing them with the outputs from a linear variable differential transformer sensor, which was mounted on the beam during an indoor experiment. The results showed that the datasets obtained from both the sensors were acceptable for nominal deflections of 3 mm and above because the error range was less than ±10%. However, the result obtained from the TLS were better than those obtained from the DC.

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2D quantitative analysis of fractures from high-resolution photos for the geomechanical characterization of rock masses

10.5194/egusphere-egu21-2589 ◽

2021 ◽

Author(s):

Emmanuel Wyser ◽

Lidia Loiotine ◽

Charlotte Wolff ◽

Gioacchino Francesco Andriani ◽

Michel Jaboyedoff ◽

...

Keyword(s):

High Resolution ◽

Point Clouds ◽

Mitigation Measures ◽

Future Perspective ◽

Rock Masses ◽

Sampling Errors ◽

Discontinuity Sets ◽

Adverse Weather ◽

Geomechanical Characterization

The identification of discontinuity sets and their properties is among the key factors for the geomechanical characterization of rock masses, which is fundamental for performing stability analyses, and for planning prevention and mitigation measures as well. In practice, discontinuity data are collected throughout difficult and time-consuming field surveys, especially when dealing with areas of wide extension, difficult accessibility, covered by dense vegetation, or with adverse weather conditions. Consequently, even experienced operators may introduce sampling errors or misinterpretations, leading to biased geomechanical models for the investigated rock mass. In the last decades, new remote techniques such as photogrammetry, Light Detection and Ranging (LiDAR), Unmanned Aerial Vehicle (UAV) and InfraRed Thermography (IRT) have been introduced to overcome the limits of conventional surveys. We propose here a new tool for extracting information on the fracture pattern in rock masses, based on remote sensing methods, with particular reference to the analysis of high-resolution georeferenced photos. The first step consists in applying the Structure from Motion (SfM) technique on photos acquired by means of digital cameras and UAV techniques. Once aligned and georeferenced, the orthophotos are exported in a GIS software, to draw the fracture traces at an appropriate scale. We developed a MATLAB routine to extract information on the geostructural setting of rock masses by performing a quantitative 2D analysis of the fracture traces, based on formulas reported in the literature. The code was written by testing few experimental and simple traces and was successively validated on an orthophoto from a real case study. Currently, the script plots the fracture traces as polylines and calculates their orientation (strike) and length. Subsequently, it detects the main discontinuity sets by fitting an experimental composite Gaussian curve on histograms showing the number of discontinuities according to their orientation, and splitting the curve in simpler Gaussian curves, with peaks corresponding to the main discontinuity sets. Then, for each set, a linear scanline intersecting the highest number of traces is plotted, and the apparent and real spacing are calculated. In a second step, a grid of circular scanlines covering the whole area where the traces are located is plotted, and the mean trace intensity, trace density and trace length estimators are calculated. It is expected to test the presented tools on other case studies, in order to optimize them and calculate additional metrics, such as persistence and block sizes, useful to the geomechanical characterization of rock masses. As a future perspective, a similar approach could be investigated for 3D analyses from point clouds.

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Segmentation and Multi-Scale Convolutional Neural Network-Based Classification of Airborne Laser Scanner Data

Sensors ◽

10.3390/s18103347 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3347 ◽

Cited By ~ 14

Author(s):

Zhishuang Yang ◽

Bo Tan ◽

Huikun Pei ◽

Wanshou Jiang

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Laser Scanning ◽

Laser Scanner ◽

Region Growing ◽

Point Clouds ◽

Classification Method ◽

Multi Scale ◽

Airborne Laser

The classification of point clouds is a basic task in airborne laser scanning (ALS) point cloud processing. It is quite a challenge when facing complex observed scenes and irregular point distributions. In order to reduce the computational burden of the point-based classification method and improve the classification accuracy, we present a segmentation and multi-scale convolutional neural network-based classification method. Firstly, a three-step region-growing segmentation method was proposed to reduce both under-segmentation and over-segmentation. Then, a feature image generation method was used to transform the 3D neighborhood features of a point into a 2D image. Finally, feature images were treated as the input of a multi-scale convolutional neural network for training and testing tasks. In order to obtain performance comparisons with existing approaches, we evaluated our framework using the International Society for Photogrammetry and Remote Sensing Working Groups II/4 (ISPRS WG II/4) 3D labeling benchmark tests. The experiment result, which achieved 84.9% overall accuracy and 69.2% of average F1 scores, has a satisfactory performance over all participating approaches analyzed.

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