scholarly journals INDOOR NAVIGATION FROM POINT CLOUDS: 3D MODELLING AND OBSTACLE DETECTION

2016 ◽  
Vol XLI-B4 ◽  
pp. 275-281 ◽  
Author(s):  
L. Díaz-Vilariño ◽  
P. Boguslawski ◽  
K. Khoshelham ◽  
H. Lorenzo ◽  
L. Mahdjoubi
Keyword(s):  
Laser Scanner ◽  
Route Planning ◽  
Point Clouds ◽  
3D Modelling ◽  
Obstacle Detection ◽  
Indoor Navigation ◽  
3D Point Clouds ◽  
Emergency Assistance ◽  
Planning Methodology ◽  
Real State

In the recent years, indoor modelling and navigation has become a research of interest because many stakeholders require navigation assistance in various application scenarios. The navigational assistance for blind or wheelchair people, building crisis management such as fire protection, augmented reality for gaming, tourism or training emergency assistance units are just some of the direct applications of indoor modelling and navigation. <br><br> Navigational information is traditionally extracted from 2D drawings or layouts. Real state of indoors, including opening position and geometry for both windows and doors, and the presence of obstacles is commonly ignored. <br><br> In this work, a real indoor-path planning methodology based on 3D point clouds is developed. The value and originality of the approach consist on considering point clouds not only for reconstructing semantically-rich 3D indoor models, but also for detecting potential obstacles in the route planning and using these for readapting the routes according to the real state of the indoor depictured by the laser scanner.

scholarly journals INDOOR NAVIGATION FROM POINT CLOUDS: 3D MODELLING AND OBSTACLE DETECTION

2016 ◽  
Vol XLI-B4 ◽  
pp. 275-281 ◽  
Author(s):  
L. Díaz-Vilariño ◽  
P. Boguslawski ◽  
K. Khoshelham ◽  
H. Lorenzo ◽  
L. Mahdjoubi
Keyword(s):  
Laser Scanner ◽  
Route Planning ◽  
Point Clouds ◽  
3D Modelling ◽  
Obstacle Detection ◽  
Indoor Navigation ◽  
3D Point Clouds ◽  
Emergency Assistance ◽  
Planning Methodology ◽  
Real State

In the recent years, indoor modelling and navigation has become a research of interest because many stakeholders require navigation assistance in various application scenarios. The navigational assistance for blind or wheelchair people, building crisis management such as fire protection, augmented reality for gaming, tourism or training emergency assistance units are just some of the direct applications of indoor modelling and navigation. &lt;br&gt;&lt;br&gt; Navigational information is traditionally extracted from 2D drawings or layouts. Real state of indoors, including opening position and geometry for both windows and doors, and the presence of obstacles is commonly ignored. &lt;br&gt;&lt;br&gt; In this work, a real indoor-path planning methodology based on 3D point clouds is developed. The value and originality of the approach consist on considering point clouds not only for reconstructing semantically-rich 3D indoor models, but also for detecting potential obstacles in the route planning and using these for readapting the routes according to the real state of the indoor depictured by the laser scanner.

scholarly journals Laser-Based Obstacle Detection at Railway Level Crossings

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Vítor Amaral ◽  
Francisco Marques ◽  
André Lourenço ◽  
José Barata ◽  
Pedro Santana
Keyword(s):  
Open Problem ◽  
Laser Scanner ◽  
Point Clouds ◽  
Obstacle Detection ◽  
Background Model ◽  
Training Phase ◽  
Level Crossing ◽  
Level Crossings ◽  
3D Point Clouds ◽  
Laser Scanners

This paper presents a system for obstacle detection in railway level crossings from 3D point clouds acquired with tilting 2D laser scanners. Although large obstacles in railway level crossings are detectable with current solutions, the detection of small obstacles remains an open problem. By relying on a tilting laser scanner, the proposed system is able to acquire highly dense and accurate point clouds, enabling the detection of small obstacles, like rocks laying near the rail. During an offline training phase, the system learns a background model of the level crossing from a set of point clouds. Then, online, obstacles are detected as occupied space contrasting with the background model. To reduce the need for manual on-site calibration, the system automatically estimates the pose of the level crossing and railway with respect to the laser scanner. Experimental results show the ability of the system to successfully perform on a set of 41 point clouds acquired in an operational one-lane level crossing.

scholarly journals Structure from motion (SfM) photogrammetry as alternative to laser scanning for 3D modelling of historical monuments

2020 ◽  
Vol 5 (2) ◽  
Author(s):  
Omar Al Khalil
Keyword(s):  
Structure From Motion ◽  
Laser Scanning ◽  
Low Cost ◽  
Laser Scanner ◽  
Digital Camera ◽  
Point Clouds ◽  
3D Modelling ◽  
3D Point Clouds ◽  
Historical Monuments ◽  
Culture Heritage

During the past few years, new developments have occurred in the field of 3D photogrammetric modeling of culture heritage. One of these developments is the expansion of 3D photogrammetric modeling open-source software, such as VisualSfM, and cost-effective licensed software, such as Agisoft Metashape into the practical and affordable world. This type of SfM (Structure from Motion) software offers the world of 3D modelling of culture heritage a powerful tool for documentation and visualization. On the other hand, low-cost cameras are now available on the market. These cameras are characterized by high resolution and good quality lens, which makes them suitable for photogrammetric modelling. This paper reports on the results of the application of a SfM photogrammetry system in the 3D modelling of Safita Tower, a medieval structure in Safita, north-western Syria. The applied photogrammetric system consists of the Nikon Coolpix P100 10 MP digital camera and the commercial software Agisoft Metashape. The resulted 3D point clouds were compared with an available dense point cloud acquired by a laser scanner. This comparison proved that the low-cost SfM photogrammetry is an accurate methodology to 3D modeling historical monuments. 

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

Sensors ◽  
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.

scholarly journals Estimating tree stem diameters and volume from smartphone photogrammetric point clouds

2019 ◽  
Vol 93 (3) ◽  
pp. 411-429 ◽  
Author(s):  
Maria Immacolata Marzulli ◽  
Pasi Raumonen ◽  
Roberto Greco ◽  
Manuela Persia ◽  
Patrizia Tartarino
Keyword(s):  
Low Cost ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Point Clouds ◽  
Stem Volume ◽  
Forest Plot ◽  
3D Point Clouds ◽  
Dense Point ◽  
Minimum Number ◽  
Scale Point

Abstract Methods for the three-dimensional (3D) reconstruction of forest trees have been suggested for data from active and passive sensors. Laser scanner technologies have become popular in the last few years, despite their high costs. Since the improvements in photogrammetric algorithms (e.g. structure from motion—SfM), photographs have become a new low-cost source of 3D point clouds. In this study, we use images captured by a smartphone camera to calculate dense point clouds of a forest plot using SfM. Eighteen point clouds were produced by changing the densification parameters (Image scale, Point density, Minimum number of matches) in order to investigate their influence on the quality of the point clouds produced. In order to estimate diameter at breast height (d.b.h.) and stem volumes, we developed an automatic method that extracts the stems from the point cloud and then models them with cylinders. The results show that Image scale is the most influential parameter in terms of identifying and extracting trees from the point clouds. The best performance with cylinder modelling from point clouds compared to field data had an RMSE of 1.9 cm and 0.094 m3, for d.b.h. and volume, respectively. Thus, for forest management and planning purposes, it is possible to use our photogrammetric and modelling methods to measure d.b.h., stem volume and possibly other forest inventory metrics, rapidly and without felling trees. The proposed methodology significantly reduces working time in the field, using ‘non-professional’ instruments and automating estimates of dendrometric parameters.

scholarly journals ACCURACY ASSESSMENT OF UNDERWATER PHOTOGRAMMETRIC THREE DIMENSIONAL MODELLING FOR CORAL REEFS

2016 ◽  
Vol XLI-B5 ◽  
pp. 821-828 ◽  
Author(s):  
T. Guo ◽  
A. Capra ◽  
M. Troyer ◽  
A. Gruen ◽  
A. J. Brooks ◽  
...  
Keyword(s):  
Coral Cover ◽  
Accuracy Assessment ◽  
Low Cost ◽  
Three Dimensional ◽  
Point Clouds ◽  
3D Modelling ◽  
Relative Accuracy ◽  
3D Point Clouds ◽  
The Individual ◽  
Working Distance

Recent advances in automation of photogrammetric 3D modelling software packages have stimulated interest in reconstructing highly accurate 3D object geometry in unconventional environments such as underwater utilizing simple and low-cost camera systems. The accuracy of underwater 3D modelling is affected by more parameters than in single media cases. This study is part of a larger project on 3D measurements of temporal change of coral cover in tropical waters. It compares the accuracies of 3D point clouds generated by using images acquired from a system camera mounted in an underwater housing and the popular GoPro cameras respectively. A precisely measured calibration frame was placed in the target scene in order to provide accurate control information and also quantify the errors of the modelling procedure. In addition, several objects (cinder blocks) with various shapes were arranged in the air and underwater and 3D point clouds were generated by automated image matching. These were further used to examine the relative accuracy of the point cloud generation by comparing the point clouds of the individual objects with the objects measured by the system camera in air (the best possible values). Given a working distance of about 1.5 m, the GoPro camera can achieve a relative accuracy of 1.3 mm in air and 2.0 mm in water. The system camera achieved an accuracy of 1.8 mm in water, which meets our requirements for coral measurement in this system.

A novel method for extracting skeleton of fruit tree from 3D point clouds

2020 ◽  
pp. 2050051
Author(s):  
Shenglian lu ◽  
Guo Li ◽  
Jian Wang
Keyword(s):  
Laser Scanning ◽  
Laser Scanner ◽  
Point Clouds ◽  
Fruit Trees ◽  
Fruit Tree ◽  
Tree Canopy ◽  
3D Point Clouds ◽  
Novel Method ◽  
Manual Interaction ◽  
Traditional Approaches

Tree skeleton could be useful to agronomy researchers because the skeleton describes the shape and topological structure of a tree. The phenomenon of organs’ mutual occlusion in fruit tree canopy is usually very serious, this should result in a large amount of data missing in directed laser scanning 3D point clouds from a fruit tree. However, traditional approaches can be ineffective and problematic in extracting the tree skeleton correctly when the tree point clouds contain occlusions and missing points. To overcome this limitation, we present a method for accurate and fast extracting the skeleton of fruit tree from laser scanner measured 3D point clouds. The proposed method selects the start point and endpoint of a branch from the point clouds by user’s manual interaction, then a backward searching is used to find a path from the 3D point cloud with a radius parameter as a restriction. The experimental results in several kinds of fruit trees demonstrate that our method can extract the skeleton of a leafy fruit tree with highly accuracy.

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.

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