Tunneling Appropriate Computational Models from Laser Scanning Data

Tunneling projects often require computational models of existing structures. To this end, this paper demonstrates the viability of automatically, robustly reconstructing an individual building model from laser scanning data for further computational modeling without any manual intervention. The resulting model is appropriate for immediate importation into a commercial finite element method (FEM) program. The method combines a voxel-based technique with an angle criterion. Initially, the voxelization model is used to represent the façade model, while an angle criterion is implemented to determine boundaries of the façade and its openings (doors and windows). The algorithm overcomes common problems of occlusions or artefacts that arise during data acquisition. The resulting relative errors of overall dimensions and opening areas of geometric models were less 2% and 6%, respectively, which are generally within industry standards for this type of building modeling.Science Foundation Ireland (SFI/PICA/I850); European Union Grant ERC StG 2012-307836- RETURN

Parameterization of structural faults in large historical constructions for further structural modelling thanks to laser scanning technology and computer vision algorithms

Laser scanning technology has evolved significantly in the last decade, particularly, in those applications in terrestrial environments dealing with the documentation and inspection of civil engineering and architectural constructions. Even though there exist mature procedures to convert the so-called LiDAR point clouds in CAD models or even FEM models, the current trends in the technology are related to the automation of these operations. The development of robust automatic procedures for data segmentation and interpretation it is a key aspect so that the technology can definitely be accepted as a basic, accurate, and robust tool for reverse engineering of existing constructions. This paper presents the application of laser scanning technology to the structural evaluation of the Medieval Wall of Guimarães (Portugal). This laser scanning survey was conducted with the aim of having an accurate and detailed geometrical model of the large masonry construction that includes the existing deformations and structural faults. The parameterization of structural damages was possible thanks to the highly detailed point cloud collected, and its processing using computer vision algorithms. The geometric models obtained could be used for further structural analysis of the entire wall.This work has been partially supported by the Spanish Ministry of Interior (Grant SPIP2017-02122), Spanish Ministry of Economy, Industry and Competitiveness (Grant: EUIN2017-87598), and Xunta de Galicia through grant ED431C2016‐038

Automated calibration of FEM models using LiDAR point clouds

In present work it is pretended to estimate elastic parameters of beams through the combined use of precision geomatic techniques (laser scanning) and structural behaviour simulation tools. The study has two aims, on the one hand, to develop an algorithm able to interpret automatically point clouds acquired by laser scanning systems of beams subjected to different load situations on experimental tests; and on the other hand, to minimize differences between deformation values given by simulation tools and those measured by laser scanning. In this way we will proceed to identify elastic parameters and boundary conditions of structural element so that surface stresses can be estimated more easily.Ministerio de Interior | Ref. SPIP2017-02122Ministerio de Economía, Industria y Competitividad | Ref. EUIN2017- 87598Ministerio de Educación, Cultura y Deporte | Ref. CAS15/00126Xunta de Galicia | Ref. ED431C2016‐03

A Sparsity-Inducing Optimization-Based Algorithm for Planar Patches Extraction from Noisy Point-Cloud Data

Currently, much of the manual labor needed to generate as-built Building Information Models (BIMs) of existing facilities is spent converting raw Point Cloud Datasets (PCDs) to BIMs descriptions. Automating the PCD conversion process can drastically reduce the cost of generating as-built BIMs. Due to the widespread existence of planar structures in civil infrastructures, detecting and extracting planar patches from raw PCDs is a fundamental step in the conversion pipeline from PCDs to BIMs. However, existing methods cannot effectively address both automatically detecting and extracting planar patches from infrastructure PCDs. The existing methods cannot resolve the problem due to the large scale and model complexity of civil infrastructure, or due to the requirements of extra constraints or known information. To address the problem, this paper presents a novel framework for automatically detecting and extracting planar patches from large-scale and noisy raw PCDs. The proposed method automatically detects planar structures, estimates the parametric plane models, and determines the boundaries of the planar patches. The first step recovers existing linear dependence relationships amongst points in the PCD by solving a group-sparsity inducing optimization problem. Next, a spectral clustering procedure based on the recovered linear dependence relationships segments the PCD. Then, for each segmented group, model parameters of the extracted planes are estimated via Singular Value Decomposition (SVD) and Maximum Likelihood Estimation Sample Consensus (MLESAC). Finally, the α-shape algorithm detects the boundaries of planar structures based on a projection of the data to the planar model. The proposed approach is evaluated comprehensively by experiments on two types of PCDs from real-world infrastructures, one captured directly by laser scanners and the other reconstructed from video using structure-from-motion techniques. In order to evaluate the performance comprehensively, five evaluation metrics are proposed which measure different aspects of performance. Experimental results reveal that the proposed method outperforms the existing methods, in the sense that the method automatically and accurately extracts planar patches from large-scaled raw PCDs without any extra constraints nor user assistance.This is the accepted manuscript. The final version is available from Wiley at http://onlinelibrary.wiley.com/doi/10.1111/mice.12063/abstract

Design and Development of Personal GeoServices for Universities

Personal GeoServices are emerging as an interaction paradigm linking users to information rich environments like a university campus or to Big Data sources like the Internet of Things by delivering spatially intelligent web-services. OpenStreetMap (OSM) constitutes a valuable source of spatial base-data that can be extracted, integrated, and utilised with such heterogeneous data sources for free. In this paper, we present a Personal GeoServices application built on OSM spatial data and university-specific business data for staff, faculty, and students. While generic products such as Google Maps and Google Earth enable basic forms of spatial exploration, the domain of a university campus presents specific business information needs, such as “What classes are scheduled in that room over there?” and “How can I get to Prof. Murray’s office from here?” Within the framework of the StratAG project (www.StratAG.ie), an eCampus Demonstrator was developed for the National University of Ireland Maynooth (NUIM) to assist university users in exploring and analysing their surroundings within a detailed data environment. This work describes this system in detail, discussing the usage of OSM vector data, and providing insights for developers of spatial information systems for personalised visual exploration of an area

Generating bridge geometric digital twins from point clouds

The automation of digital twinning for existing bridges from point clouds remains unsolved. Extensive manual effort is required to extract object point clusters from point clouds followed by fitting them with accurate 3D shapes. Previous research yielded methods that can automatically generate surface primitives combined with rule-based classification to create labelled cuboids and cylinders. While these methods work well in synthetic datasets or simplified cases, they encounter huge challenges when dealing with realworld point clouds. In addition, bridge geometries, defined with curved alignments and varying elevations, are much more complicated than idealized cases. None of the existing methods can handle these difficulties reliably. The proposed framework employs bridge engineering knowledge that mimics the intelligence of human modellers to detect and model reinforced concrete bridge objects in imperfect point clouds. It directly produces labelled 3D objects in Industry Foundation Classes format without generating low-level shape primitives. Experiments on ten bridge point clouds indicate the framework achieves an overall detection F1-score of 98.4%, an average modelling accuracy of 7.05 cm, and an average modelling time of merely 37.8 seconds. This is the first framework of its kind to achieve high and reliable performance of geometric digital twin generation of existing bridges

Generating bridge geometric digital twins from point clouds

The automation of digital twinning for existing bridges from point clouds remains unresolved. Previous research yielded methods that can generate surface primitives combined with rule-based classification to create labelled cuboids and cylinders. While these methods work well in synthetic datasets or simplified cases, they encounter huge challenges when dealing with real-world point clouds. The proposed framework employs bridge engineering knowledge that mimics the intelligence of human modellers to detect and model reinforced concrete bridge objects in imperfect point clouds. Experiments on ten bridge point clouds indicate the framework can achieve high and reliable performance of geometric digital twin generation of existing bridges.This research is funded by EPSRC, EU Infravation SeeBridge project under Grant No. 31109806.0007 and Trimble Research Fun

Algorithms for the reconstruction, analysis, repairing and enhancement of 3D urban models from multiple data sources

Over the last few years, there has been a notorious growth in the field of digitization of 3D buildings and urban environments. The substantial improvement of both scanning hardware and reconstruction algorithms has led to the development of representations of buildings and cities that can be remotely transmitted and inspected in real-time. Among the applications that implement these technologies are several GPS navigators and virtual globes such as Google Earth or the tools provided by the Institut Cartogràfic i Geològic de Catalunya. In particular, in this thesis, we conceptualize cities as a collection of individual buildings. Hence, we focus on the individual processing of one structure at a time, rather than on the larger-scale processing of urban environments. Nowadays, there is a wide diversity of digitization technologies, and the choice of the appropriate one is key for each particular application. Roughly, these techniques can be grouped around three main families: - Time-of-flight (terrestrial and aerial LiDAR). - Photogrammetry (street-level, satellite, and aerial imagery). - Human-edited vector data (cadastre and other map sources). Each of these has its advantages in terms of covered area, data quality, economic cost, and processing effort. Plane and car-mounted LiDAR devices are optimal for sweeping huge areas, but acquiring and calibrating such devices is not a trivial task. Moreover, the capturing process is done by scan lines, which need to be registered using GPS and inertial data. As an alternative, terrestrial LiDAR devices are more accessible but cover smaller areas, and their sampling strategy usually produces massive point clouds with over-represented plain regions. A more inexpensive option is street-level imagery. A dense set of images captured with a commodity camera can be fed to state-of-the-art multi-view stereo algorithms to produce realistic-enough reconstructions. One other advantage of this approach is capturing high-quality color data, whereas the geometric information is usually lacking. In this thesis, we analyze in-depth some of the shortcomings of these data-acquisition methods and propose new ways to overcome them. Mainly, we focus on the technologies that allow high-quality digitization of individual buildings. These are terrestrial LiDAR for geometric information and street-level imagery for color information. Our main goal is the processing and completion of detailed 3D urban representations. For this, we will work with multiple data sources and combine them when possible to produce models that can be inspected in real-time. Our research has focused on the following contributions: - Effective and feature-preserving simplification of massive point clouds. - Developing normal estimation algorithms explicitly designed for LiDAR data. - Low-stretch panoramic representation for point clouds. - Semantic analysis of street-level imagery for improved multi-view stereo reconstruction. - Color improvement through heuristic techniques and the registration of LiDAR and imagery data. - Efficient and faithful visualization of massive point clouds using image-based techniques.Durant els darrers anys, hi ha hagut un creixement notori en el camp de la digitalització d'edificis en 3D i entorns urbans. La millora substancial tant del maquinari d'escaneig com dels algorismes de reconstrucció ha portat al desenvolupament de representacions d'edificis i ciutats que es poden transmetre i inspeccionar remotament en temps real. Entre les aplicacions que implementen aquestes tecnologies hi ha diversos navegadors GPS i globus virtuals com Google Earth o les eines proporcionades per l'Institut Cartogràfic i Geològic de Catalunya. En particular, en aquesta tesi, conceptualitzem les ciutats com una col·lecció d'edificis individuals. Per tant, ens centrem en el processament individual d'una estructura a la vegada, en lloc del processament a gran escala d'entorns urbans. Avui en dia, hi ha una àmplia diversitat de tecnologies de digitalització i la selecció de l'adequada és clau per a cada aplicació particular. Aproximadament, aquestes tècniques es poden agrupar en tres famílies principals: - Temps de vol (LiDAR terrestre i aeri). - Fotogrametria (imatges a escala de carrer, de satèl·lit i aèries). - Dades vectorials editades per humans (cadastre i altres fonts de mapes). Cadascun d'ells presenta els seus avantatges en termes d'àrea coberta, qualitat de les dades, cost econòmic i esforç de processament. Els dispositius LiDAR muntats en avió i en cotxe són òptims per escombrar àrees enormes, però adquirir i calibrar aquests dispositius no és una tasca trivial. A més, el procés de captura es realitza mitjançant línies d'escaneig, que cal registrar mitjançant GPS i dades inercials. Com a alternativa, els dispositius terrestres de LiDAR són més accessibles, però cobreixen àrees més petites, i la seva estratègia de mostreig sol produir núvols de punts massius amb regions planes sobrerepresentades. Una opció més barata són les imatges a escala de carrer. Es pot fer servir un conjunt dens d'imatges capturades amb una càmera de qualitat mitjana per obtenir reconstruccions prou realistes mitjançant algorismes estèreo d'última generació per produir. Un altre avantatge d'aquest mètode és la captura de dades de color d'alta qualitat. Tanmateix, la informació geomètrica resultant sol ser de baixa qualitat. En aquesta tesi, analitzem en profunditat algunes de les mancances d'aquests mètodes d'adquisició de dades i proposem noves maneres de superar-les. Principalment, ens centrem en les tecnologies que permeten una digitalització d'alta qualitat d'edificis individuals. Es tracta de LiDAR terrestre per obtenir informació geomètrica i imatges a escala de carrer per obtenir informació sobre colors. El nostre objectiu principal és el processament i la millora de representacions urbanes 3D amb molt detall. Per a això, treballarem amb diverses fonts de dades i les combinarem quan sigui possible per produir models que es puguin inspeccionar en temps real. La nostra investigació s'ha centrat en les següents contribucions: - Simplificació eficaç de núvols de punts massius, preservant detalls d'alta resolució. - Desenvolupament d'algoritmes d'estimació normal dissenyats explícitament per a dades LiDAR. - Representació panoràmica de baixa distorsió per a núvols de punts. - Anàlisi semàntica d'imatges a escala de carrer per millorar la reconstrucció estèreo de façanes. - Millora del color mitjançant tècniques heurístiques i el registre de dades LiDAR i imatge. - Visualització eficient i fidel de núvols de punts massius mitjançant tècniques basades en imatges

3d virtual modelling of existing objects by terrestrial photogrammetric methods - case study of Barutana

Three dimensional virtual modelling of existing objects (buildings or structures) is applicable in various fields of science and practice: architecture, civil engineering, urbanism, geology, mechanical engineering, video games and movie industry, medicine, archeology, safety of people and goods, etc. Photogrammetry, as a method of obtaining data of three-dimensional spatial structures based on two-dimensional images, is used, thanks to a number of software packages, for creating 3D models of objects and other spatial structures. This study analyses terrestrial semiautomatic and automatic photogrammetric methods, both presented through process of creating 3D model of an old existing historical building - Barutana (military gun powder warehouse), built in Ottoman empire, located in the fortress of the city of Nis in Serbia. The aim of the paper is comparison of two photogrammetric methods - semiautomatic and automatic in accuracy and efficiency through case study of Barutana