scholarly journals DETECTING AND ANALYZING CORROSION SPOTS ON THE HULL OF LARGE MARINE VESSELS USING COLORED 3D LIDAR POINT CLOUDS

2016 ◽  
Vol III-3 ◽  
pp. 153-160 ◽  
Author(s):  
A. K. Aijazi ◽  
L. Malaterre ◽  
M. L. Tazir ◽  
L. Trassoudaine ◽  
P. Checchin
Keyword(s):  
Surface Defects ◽  
Real Data ◽  
Point Clouds ◽  
Color Component ◽  
Ship Hulls ◽  
Adaptive Thresholds ◽  
Different Shapes ◽  
The Cost ◽  
Marine Vessels ◽  
3D Lidar

This work presents a new method that automatically detects and analyzes surface defects such as corrosion spots of different shapes and sizes, on large ship hulls. In the proposed method several scans from different positions and viewing angles around the ship are registered together to form a complete 3D point cloud. The <i>R</i>, <i>G</i>, <i>B</i> values associated with each scan, obtained with the help of an integrated camera are converted into HSV space to separate out the illumination invariant color component from the intensity. Using this color component, different surface defects such as corrosion spots of different shapes and sizes are automatically detected, within a selected zone, using two different methods depending upon the level of corrosion/defects. The first method relies on a histogram based distribution whereas the second on adaptive thresholds. The detected corrosion spots are then analyzed and quantified to help better plan and estimate the cost of repair and maintenance. Results are evaluated on real data using different standard evaluation metrics to demonstrate the efficacy as well as the technical strength of the proposed method.

scholarly journals DETECTING AND ANALYZING CORROSION SPOTS ON THE HULL OF LARGE MARINE VESSELS USING COLORED 3D LIDAR POINT CLOUDS

2016 ◽  
Vol III-3 ◽  
pp. 153-160 ◽  
Author(s):  
A. K. Aijazi ◽  
L. Malaterre ◽  
M. L. Tazir ◽  
L. Trassoudaine ◽  
P. Checchin
Keyword(s):  
Surface Defects ◽  
Real Data ◽  
Point Clouds ◽  
Color Component ◽  
Ship Hulls ◽  
Adaptive Thresholds ◽  
Different Shapes ◽  
The Cost ◽  
Marine Vessels ◽  
3D Lidar

This work presents a new method that automatically detects and analyzes surface defects such as corrosion spots of different shapes and sizes, on large ship hulls. In the proposed method several scans from different positions and viewing angles around the ship are registered together to form a complete 3D point cloud. The <i>R</i>, <i>G</i>, <i>B</i> values associated with each scan, obtained with the help of an integrated camera are converted into HSV space to separate out the illumination invariant color component from the intensity. Using this color component, different surface defects such as corrosion spots of different shapes and sizes are automatically detected, within a selected zone, using two different methods depending upon the level of corrosion/defects. The first method relies on a histogram based distribution whereas the second on adaptive thresholds. The detected corrosion spots are then analyzed and quantified to help better plan and estimate the cost of repair and maintenance. Results are evaluated on real data using different standard evaluation metrics to demonstrate the efficacy as well as the technical strength of the proposed method.

scholarly journals Automatic Detection and Feature Estimation of Windows for Refining Building Facades in 3D Urban Point Clouds

2014 ◽  
Vol II-3 ◽  
pp. 1-8 ◽  
Author(s):  
A.K Aijazi ◽  
P. Checchin ◽  
L. Trassoudaine
Keyword(s):  
Point Cloud ◽  
Real Data ◽  
Point Clouds ◽  
Building Facade ◽  
Building Facades ◽  
Geometrical Information ◽  
Different Shapes ◽  
Feature Estimation ◽  
3D Lidar ◽  
Terrestrial Data

This paper presents a method that automatically detects windows of different shapes, in 3D LiDAR point clouds obtained from mobile terrestrial data acquisition systems in the urban environment. The proposed method first segments out 3D points belonging to the building façade from the 3D urban point cloud and then projects them onto a 2D plane parallel to the building façade. After point inversion within a watertight boundary, windows are segmented out based on geometrical information. The window features/parameters are then estimated exploiting both symmetrically corresponding windows in the façade as well as temporally corresponding windows in successive passages, based on analysis of variance measurements. This unique fusion of information not only accommodates for lack of symmetry but also helps complete missing features due to occlusions. The estimated windows are then used to refine the 3D point cloud of the building façade. The results, evaluated on real data using different standard evaluation metrics, demonstrate the efficacy as well as the technical prowess of the method.

scholarly journals DISOCCLUSION OF 3D LIDAR POINT CLOUDS USING RANGE IMAGES

2017 ◽  
Vol IV-1/W1 ◽  
pp. 75-82
Author(s):  
P. Biasutti ◽  
J.-F. Aujol ◽  
M. Brédif ◽  
A. Bugeau
Keyword(s):  
Point Cloud ◽  
Automatic Segmentation ◽  
Image Inpainting ◽  
Real Data ◽  
Point Clouds ◽  
Range Image ◽  
3D Point Cloud ◽  
3D Space ◽  
Variational Image ◽  
3D Lidar

This paper proposes a novel framework for the disocclusion of mobile objects in 3D LiDAR scenes aquired via street-based Mobile Mapping Systems (MMS). Most of the existing lines of research tackle this problem directly in the 3D space. This work promotes an alternative approach by using a 2D range image representation of the 3D point cloud, taking advantage of the fact that the problem of disocclusion has been intensively studied in the 2D image processing community over the past decade. First, the point cloud is turned into a 2D range image by exploiting the sensor’s topology. Using the range image, a semi-automatic segmentation procedure based on depth histograms is performed in order to select the occluding object to be removed. A variational image inpainting technique is then used to reconstruct the area occluded by that object. Finally, the range image is unprojected as a 3D point cloud. Experiments on real data prove the effectiveness of this procedure both in terms of accuracy and speed.

scholarly journals DATA-DRIVEN MODELING OF BUILDING INTERIORS FROM LIDAR POINT CLOUDS

2020 ◽  
Vol V-2-2020 ◽  
pp. 395-402
Author(s):  
J. Sanchez ◽  
F. Denis ◽  
F. Dupont ◽  
L. Trassoudaine ◽  
P. Checchin
Keyword(s):  
Point Cloud ◽  
State Of The Art ◽  
Real Data ◽  
Point Clouds ◽  
3D Models ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Automatic Methods ◽  
Data Driven Modeling ◽  
3D Lidar

Abstract. This paper deals with 3D modeling of building interiors from point clouds captured by a 3D LiDAR scanner. Indeed, currently, the building reconstruction processes remain mostly manual. While LiDAR data have some specific properties which make the reconstruction challenging (anisotropy, noise, clutters, etc.), the automatic methods of the state-of-the-art rely on numerous construction hypotheses which yield 3D models relatively far from initial data. The choice has been done to propose a new modeling method closer to point cloud data, reconstructing only scanned areas of each scene and excluding occluded regions. According to this objective, our approach reconstructs LiDAR scans individually using connected polygons. This modeling relies on a joint processing of an image created from the 2D LiDAR angular sampling and the 3D point cloud associated to one scan. Results are evaluated on synthetic and real data to demonstrate the efficiency as well as the technical strength of the proposed method.

Pareto Set as a Model for Dispatching Resources in Emergency Centres (Preprint)

2018 ◽  
Author(s):  
Ricardo Guedes ◽  
Vasco Furtado ◽  
Tarcísio Pequeno ◽  
Joel Rodrigues
Keyword(s):  
Operational Research ◽  
Real Data ◽  
Pareto Set ◽  
Multi Objective Optimization ◽  
Pareto Dominance ◽  
Four Dimensions ◽  
Multi Objective ◽  
The Best Approximation ◽  
One Year ◽  
The Cost

UNSTRUCTURED The article investigates policies for helping emergency-centre authorities for dispatching resources aimed at reducing goals such as response time, the number of unattended calls, the attending of priority calls, and the cost of displacement of vehicles. Pareto Set is shown to be the appropriated way to support the representation of policies of dispatch since it naturally fits the challenges of multi-objective optimization. By means of the concept of Pareto dominance a set with objectives may be ordered in a way that guides the dispatch of resources. Instead of manually trying to identify the best dispatching strategy, a multi-objective evolutionary algorithm coupled with an Emergency Call Simulator uncovers automatically the best approximation of the optimal Pareto Set that would be the responsible for indicating the importance of each objective and consequently the order of attendance of the calls. The scenario of validation is a big metropolis in Brazil using one-year of real data from 911 calls. Comparisons with traditional policies proposed in the literature are done as well as other innovative policies inspired from different domains as computer science and operational research. The results show that strategy of ranking the calls from a Pareto Set discovered by the evolutionary method is a good option because it has the second best (lowest) waiting time, serves almost 100% of priority calls, is the second most economical, and is the second in attendance of calls. That is to say, it is a strategy in which the four dimensions are considered without major impairment to any of them.

scholarly journals A Closed-Form Solution to Planar Feature-Based Registration of LiDAR Point Clouds

2021 ◽  
Vol 10 (7) ◽  
pp. 435
Author(s):  
Yongbo Wang ◽  
Nanshan Zheng ◽  
Zhengfu Bian
Keyword(s):  
Closed Form ◽  
Closed Form Solution ◽  
Simulated Data ◽  
Real Data ◽  
Point Clouds ◽  
Form Solution ◽  
Spatial Transformation ◽  
Dual Quaternions ◽  
Feature Based ◽  
Planar Feature

Since pairwise registration is a necessary step for the seamless fusion of point clouds from neighboring stations, a closed-form solution to planar feature-based registration of LiDAR (Light Detection and Ranging) point clouds is proposed in this paper. Based on the Plücker coordinate-based representation of linear features in three-dimensional space, a quad tuple-based representation of planar features is introduced, which makes it possible to directly determine the difference between any two planar features. Dual quaternions are employed to represent spatial transformation and operations between dual quaternions and the quad tuple-based representation of planar features are given, with which an error norm is constructed. Based on L2-norm-minimization, detailed derivations of the proposed solution are explained step by step. Two experiments were designed in which simulated data and real data were both used to verify the correctness and the feasibility of the proposed solution. With the simulated data, the calculated registration results were consistent with the pre-established parameters, which verifies the correctness of the presented solution. With the real data, the calculated registration results were consistent with the results calculated by iterative methods. Conclusions can be drawn from the two experiments: (1) The proposed solution does not require any initial estimates of the unknown parameters in advance, which assures the stability and robustness of the solution; (2) Using dual quaternions to represent spatial transformation greatly reduces the additional constraints in the estimation process.

scholarly journals ALPINE: Active Link Prediction Using Network Embedding

2021 ◽  
Vol 11 (11) ◽  
pp. 5043
Author(s):  
Xi Chen ◽  
Bo Kang ◽  
Jefrey Lijffijt ◽  
Tijl De Bie
Keyword(s):  
Active Learning ◽  
Protein Interactions ◽  
Link Prediction ◽  
Prediction Accuracy ◽  
Real Data ◽  
Network Embedding ◽  
Protein Protein Interactions ◽  
Additional Information ◽  
The Cost ◽  
Active Link

Many real-world problems can be formalized as predicting links in a partially observed network. Examples include Facebook friendship suggestions, the prediction of protein–protein interactions, and the identification of hidden relationships in a crime network. Several link prediction algorithms, notably those recently introduced using network embedding, are capable of doing this by just relying on the observed part of the network. Often, whether two nodes are linked can be queried, albeit at a substantial cost (e.g., by questionnaires, wet lab experiments, or undercover work). Such additional information can improve the link prediction accuracy, but owing to the cost, the queries must be made with due consideration. Thus, we argue that an active learning approach is of great potential interest and developed ALPINE (Active Link Prediction usIng Network Embedding), a framework that identifies the most useful link status by estimating the improvement in link prediction accuracy to be gained by querying it. We proposed several query strategies for use in combination with ALPINE, inspired by the optimal experimental design and active learning literature. Experimental results on real data not only showed that ALPINE was scalable and boosted link prediction accuracy with far fewer queries, but also shed light on the relative merits of the strategies, providing actionable guidance for practitioners.

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

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.

scholarly journals Parallel Point Clouds: Hybrid Point Cloud Generation and 3D Model Enhancement via Virtual–Real Integration

2021 ◽  
Vol 13 (15) ◽  
pp. 2868
Author(s):  
Yonglin Tian ◽  
Xiao Wang ◽  
Yu Shen ◽  
Zhongzheng Guo ◽  
Zilei Wang ◽  
...  
Keyword(s):  
Point Cloud ◽  
Closed Loop ◽  
Real Data ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Training Model ◽  
Labor Cost ◽  
Open Loop ◽  
Training Dataset ◽  
Data Annotation

Three-dimensional information perception from point clouds is of vital importance for improving the ability of machines to understand the world, especially for autonomous driving and unmanned aerial vehicles. Data annotation for point clouds is one of the most challenging and costly tasks. In this paper, we propose a closed-loop and virtual–real interactive point cloud generation and model-upgrading framework called Parallel Point Clouds (PPCs). To our best knowledge, this is the first time that the training model has been changed from an open-loop to a closed-loop mechanism. The feedback from the evaluation results is used to update the training dataset, benefiting from the flexibility of artificial scenes. Under the framework, a point-based LiDAR simulation model is proposed, which greatly simplifies the scanning operation. Besides, a group-based placing method is put forward to integrate hybrid point clouds, via locating candidate positions for virtual objects in real scenes. Taking advantage of the CAD models and mobile LiDAR devices, two hybrid point cloud datasets, i.e., ShapeKITTI and MobilePointClouds, are built for 3D detection tasks. With almost zero labor cost on data annotation for newly added objects, the models (PointPillars) trained with ShapeKITTI and MobilePointClouds achieved 78.6% and 60.0% of the average precision of the model trained with real data on 3D detection, respectively.

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

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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