Super edge 4-points congruent sets-based point cloud global registration

With the acceleration in three-dimensional (3D) high-frame-rate sensing technologies, dense point clouds collected from multiple standpoints pose a great challenge for the accuracy and efficiency of registration. The combination of coarse registration and fine registration has been extensively promoted. Unlike the requirement of small movements between scan pairs in fine registration, coarse registration can match scans with arbitrary initial poses. The state-of-the-art coarse methods, Super 4-Points Congruent Sets algorithm based on the 4-Points Congruent Sets, improves the speed of registration to a linear order via smart indexing. However, the lack of reduction in the scale of original point clouds limits the application. Besides, the coplanarity of registration bases prevents further reduction of search space. This paper proposes a novel registration method called the Super Edge 4-Points Congruent Sets to address the above problems. The proposed algorithm follows a three-step procedure, including boundary segmentation, overlapping regions extraction, and bases selection. Firstly, an improved method based on vector angle is used to segment the original point clouds aiming to thin out the scale of the initial point clouds. Furthermore, overlapping regions extraction is executed to find out the overlapping regions on the contour. Finally, the proposed method selects registration bases conforming to the distance constraints from the candidate set without consideration about coplanarity. Experiments on various datasets with different characteristics have demonstrated that the average time complexity of the proposed algorithm is improved by 89.76%, and the accuracy is improved by 5 mm on average than the Super 4-Points Congruent Sets algorithm. More encouragingly, the experimental results show that the proposed algorithm can be applied to various restrictive cases, such as few overlapping regions and massive noise. Therefore, the algorithm proposed in this paper is a faster and more robust method than Super 4-Points Congruent Sets under the guarantee of the promised quality.</jats:p

Detection and localisation of structural deformations using terrestrial laser scanning and generalised procrustes analysis

One of the most vital duties for engineers is to preserve life and nature by utilising safe designs that take into account environmental standards and monitoring the performance of structures against design criteria. Furthermore, monitoring can be used to determine any required maintenance of an important structure following a catastrophic event. Numerous different techniques and instruments can be employed for such a purpose with different requirements producing different results. For instance, some techniques need to embed sensors inside the building, such as Geotechnical Sensors. Others can offer high quality, but with a low point density and require fixed stations and targets, like Total Stations (TS). In such cases, the location of deformation tends to be known, such as in dams, bridges, and high-rise buildings. However, this is not always the case where it might be hard to expect deformation location as in the case of historic ruins where each part of the structure could be subject to deformation. The challenge in such case is to detect the deformation without any previous knowledge. Remote Sensing (RS) techniques, such as Digital Photogrammetry, Synthetic Aperture Radar (SAR), Interferometric Synthetic Aperture Radar (InSAR), and Terrestrial Laser Scanner (TLS) can be solutions for such an issue. Interestingly, many researchers are focusing on using TLS for monitoring owing to the great spatial resolution system can offer. However, there are three challenges in using TLS in monitoring: the first one is a huge amount of data and the difficulty of handling it; the second one is the difficulty of comparing between two epochs because observations of TLS are not repeatable; and the third issue is the noise which is attached to the data. The first problem is solved by segmentation and point structure while the second and the third ones still need more investigation, although some interesting researches have been done in this area. The aim of this research is to develop a new approach to detect and localise unpredictable deformation. It is based on TLS measurements and Generalised Procrustes Analysis (GPA) techniques to determine deformation vectors, while boxing structure and F-test are used to detect and localise deformation. In summary, after applying this approach, the whole concerned building is represented as parts, for each of which the displacement vector and the deformation probability are estimated. Ultimately, it is possible to monitor any part through different epochs. In addition, through this technique, it is possible to determine deformations - not just between two epochs, but for sequences of them. This can give more reliable results. Four validation experiments have been conducted. The first test was designed to assess the performance of the developed software and to fix some variables. Therefore, it was based on simulated data with controlled white noise, distributed according to the normal distribution, and simulated deformations. The results of this test revealed the success of the proposed algorithm to detect and to localise deformations. In addition, it showed the success when no deformations exist. Furthermore, optimistically, it could observe deformations with magnitude less than the noise level; however, the probability was only 40%. Correspondingly, real scan data with simulated deformations was used in the second test. The purpose of this test is to examine the performance of the proposed method in case of real errors budget. However, the short range of the test (about 10m), a featureless scanned area (wall only), and scanning from one position for all epochs (no need for registration) can reduce errors to a minimum. Results of this test showed the success of the proposed method to detect and localise deformations. Potentially, it can give indications for areas with deformations less than the noise level. Furthermore, results of the proposed method can be considered better than that of CloudCompare software. The third test was conducted to examine the performance of the proposed technique regarding different materials and textures. For this purpose, the Nottingham Geospatial Building (NGB) was selected with more extensive ranges (between 20-25 m). Similar to the second test, all measurements were taken from the same scanner position. To some extent, the proposed technique succeeded to detect and to localise deformations. However, the researcher does not recommend it for monitoring modern and complicated buildings, instead it has been developed for monitoring historic ruins. Finally, the proposed method was applied on the Bellmanpark Limekiln, Clitheroe, Lancashire monitoring project. This is a live project for Historic England and addresses a historic building that currently has some structural issues. The outcome of the proposed method revealed deformations in the faces South East (SE) and North East (NE). From examining these faces, three deformed areas were found: two in the face SE and one in the face NE, which might cause some cracks appeared in these faces. Alternatively, the CloudCompare software has been employed to detect deformation. Although results coincide with the proposed method for detected deformations, it cannot locate these deformations very well because it diffused over a wide area. In addition, it cannot determine actual directions of the deformations unlike the proposed method

Detection and localisation of structural deformations using terrestrial laser scanning and generalised procrustes analysis

One of the most vital duties for engineers is to preserve life and nature by utilising safe designs that take into account environmental standards and monitoring the performance of structures against design criteria. Furthermore, monitoring can be used to determine any required maintenance of an important structure following a catastrophic event. Numerous different techniques and instruments can be employed for such a purpose with different requirements producing different results. For instance, some techniques need to embed sensors inside the building, such as Geotechnical Sensors. Others can offer high quality, but with a low point density and require fixed stations and targets, like Total Stations (TS). In such cases, the location of deformation tends to be known, such as in dams, bridges, and high-rise buildings. However, this is not always the case where it might be hard to expect deformation location as in the case of historic ruins where each part of the structure could be subject to deformation. The challenge in such case is to detect the deformation without any previous knowledge. Remote Sensing (RS) techniques, such as Digital Photogrammetry, Synthetic Aperture Radar (SAR), Interferometric Synthetic Aperture Radar (InSAR), and Terrestrial Laser Scanner (TLS) can be solutions for such an issue. Interestingly, many researchers are focusing on using TLS for monitoring owing to the great spatial resolution system can offer. However, there are three challenges in using TLS in monitoring: the first one is a huge amount of data and the difficulty of handling it; the second one is the difficulty of comparing between two epochs because observations of TLS are not repeatable; and the third issue is the noise which is attached to the data. The first problem is solved by segmentation and point structure while the second and the third ones still need more investigation, although some interesting researches have been done in this area. The aim of this research is to develop a new approach to detect and localise unpredictable deformation. It is based on TLS measurements and Generalised Procrustes Analysis (GPA) techniques to determine deformation vectors, while boxing structure and F-test are used to detect and localise deformation. In summary, after applying this approach, the whole concerned building is represented as parts, for each of which the displacement vector and the deformation probability are estimated. Ultimately, it is possible to monitor any part through different epochs. In addition, through this technique, it is possible to determine deformations - not just between two epochs, but for sequences of them. This can give more reliable results. Four validation experiments have been conducted. The first test was designed to assess the performance of the developed software and to fix some variables. Therefore, it was based on simulated data with controlled white noise, distributed according to the normal distribution, and simulated deformations. The results of this test revealed the success of the proposed algorithm to detect and to localise deformations. In addition, it showed the success when no deformations exist. Furthermore, optimistically, it could observe deformations with magnitude less than the noise level; however, the probability was only 40%. Correspondingly, real scan data with simulated deformations was used in the second test. The purpose of this test is to examine the performance of the proposed method in case of real errors budget. However, the short range of the test (about 10m), a featureless scanned area (wall only), and scanning from one position for all epochs (no need for registration) can reduce errors to a minimum. Results of this test showed the success of the proposed method to detect and localise deformations. Potentially, it can give indications for areas with deformations less than the noise level. Furthermore, results of the proposed method can be considered better than that of CloudCompare software. The third test was conducted to examine the performance of the proposed technique regarding different materials and textures. For this purpose, the Nottingham Geospatial Building (NGB) was selected with more extensive ranges (between 20-25 m). Similar to the second test, all measurements were taken from the same scanner position. To some extent, the proposed technique succeeded to detect and to localise deformations. However, the researcher does not recommend it for monitoring modern and complicated buildings, instead it has been developed for monitoring historic ruins. Finally, the proposed method was applied on the Bellmanpark Limekiln, Clitheroe, Lancashire monitoring project. This is a live project for Historic England and addresses a historic building that currently has some structural issues. The outcome of the proposed method revealed deformations in the faces South East (SE) and North East (NE). From examining these faces, three deformed areas were found: two in the face SE and one in the face NE, which might cause some cracks appeared in these faces. Alternatively, the CloudCompare software has been employed to detect deformation. Although results coincide with the proposed method for detected deformations, it cannot locate these deformations very well because it diffused over a wide area. In addition, it cannot determine actual directions of the deformations unlike the proposed method

Morphology-based landslide monitoring with an unmanned aerial vehicle

PhD ThesisLandslides represent major natural phenomena with often disastrous consequences. Monitoring landslides with time-series surface observations can help mitigate such hazards. Unmanned aerial vehicles (UAVs) employing compact digital cameras, and in conjunction with Structure-from-Motion (SfM) and modern Multi-View Stereo (MVS) image matching approaches, have become commonplace in the geoscience research community. These methods offer a relatively low-cost and flexible solution for many geomorphological applications. The SfM-MVS pipeline has expedited the generation of digital elevation models at high spatio-temporal resolution. Conventionally ground control points (GCPs) are required for co-registration. This task is often expensive and impracticable considering hazardous terrain. This research has developed a strategy for processing UAV visible wavelength imagery that can provide multi-temporal surface morphological information for landslide monitoring, in an attempt to overcome the reliance on GCPs. This morphological-based strategy applies the attribute of curvature in combination with the scale-invariant feature transform algorithm, to generate pseudo GCPs. Openness is applied to extract relatively stable regions whereby pseudo GCPs are selected. Image cross-correlation functions integrated with openness and slope are employed to track landslide motion with subsequent elevation differences and planimetric surface displacements produced. Accuracy assessment evaluates unresolved biases with the aid of benchmark datasets. This approach was tested in the UK, in two sites, first in Sandford with artificial surface change and then in an active landslide at Hollin Hill. In Sandford, the strategy detected a ±0.120 m 3D surface change from three-epoch SfM-MVS products derived from a consumer-grade UAV. For the Hollin Hill landslide six-epoch datasets spanning an eighteen-month duration period were used, providing a ± 0.221 m minimum change. Annual displacement rates of dm-level were estimated with optimal results over winter periods. Levels of accuracy and spatial resolution comparable to previous studies demonstrated the potential of the morphology-based strategy for a time-efficient and cost-effective monitoring at inaccessible areas

Geomatics support to the metric documentation of the archaeological heritage. Tests and validations on the use of low-cost, rapid, image-based sensors and systems.

L'abstract è presente nell'allegato / the abstract is in the attachmen

Le nuage de point intelligent

Discrete spatial datasets known as point clouds often lay the groundwork for decision-making applications. E.g., we can use such data as a reference for autonomous cars and robot’s navigation, as a layer for floor-plan’s creation and building’s construction, as a digital asset for environment modelling and incident prediction... Applications are numerous, and potentially increasing if we consider point clouds as digital reality assets. Yet, this expansion faces technical limitations mainly from the lack of semantic information within point ensembles. Connecting knowledge sources is still a very manual and time-consuming process suffering from error-prone human interpretation. This highlights a strong need for domain-related data analysis to create a coherent and structured information. The thesis clearly tries to solve automation problematics in point cloud processing to create intelligent environments, i.e. virtual copies that can be used/integrated in fully autonomous reasoning services. We tackle point cloud questions associated with knowledge extraction – particularly segmentation and classification – structuration, visualisation and interaction with cognitive decision systems. We propose to connect both point cloud properties and formalized knowledge to rapidly extract pertinent information using domain-centered graphs. The dissertation delivers the concept of a Smart Point Cloud (SPC) Infrastructure which serves as an interoperable and modular architecture for a unified processing. It permits an easy integration to existing workflows and a multi-domain specialization through device knowledge, analytic knowledge or domain knowledge. Concepts, algorithms, code and materials are given to replicate findings and extend current applications.Les ensembles discrets de données spatiales, appelés nuages de points, forment souvent le support principal pour des scénarios d’aide à la décision. Par exemple, nous pouvons utiliser ces données comme référence pour les voitures autonomes et la navigation des robots, comme couche pour la création de plans et la construction de bâtiments, comme actif numérique pour la modélisation de l'environnement et la prédiction d’incidents... Les applications sont nombreuses et potentiellement croissantes si l'on considère les nuages de points comme des actifs de réalité numérique. Cependant, cette expansion se heurte à des limites techniques dues principalement au manque d'information sémantique au sein des ensembles de points. La création de liens avec des sources de connaissances est encore un processus très manuel, chronophage et lié à une interprétation humaine sujette à l'erreur. Cela met en évidence la nécessité d'une analyse automatisée des données relatives au domaine étudié afin de créer une information cohérente et structurée. La thèse tente clairement de résoudre les problèmes d'automatisation dans le traitement des nuages de points pour créer des environnements intelligents, c'est-àdire des copies virtuelles qui peuvent être utilisées/intégrées dans des services de raisonnement totalement autonomes. Nous abordons plusieurs problématiques liées aux nuages de points et associées à l'extraction des connaissances - en particulier la segmentation et la classification - la structuration, la visualisation et l'interaction avec les systèmes cognitifs de décision. Nous proposons de relier à la fois les propriétés des nuages de points et les connaissances formalisées pour extraire rapidement les informations pertinentes à l'aide de graphes centrés sur le domaine. La dissertation propose le concept d'une infrastructure SPC (Smart Point Cloud) qui sert d'architecture interopérable et modulaire pour un traitement unifié. Elle permet une intégration facile aux flux de travail existants et une spécialisation multidomaine grâce aux connaissances liée aux capteurs, aux connaissances analytiques ou aux connaissances de domaine. Plusieurs concepts, algorithmes, codes et supports sont fournis pour reproduire les résultats et étendre les applications actuelles.Diskrete räumliche Datensätze, so genannte Punktwolken, bilden oft die Grundlage für Entscheidungsanwendungen. Beispielsweise können wir solche Daten als Referenz für autonome Autos und Roboternavigation, als Ebene für die Erstellung von Grundrissen und Gebäudekonstruktionen, als digitales Gut für die Umgebungsmodellierung und Ereignisprognose verwenden... Die Anwendungen sind zahlreich und nehmen potenziell zu, wenn wir Punktwolken als Digital Reality Assets betrachten. Allerdings stößt diese Erweiterung vor allem durch den Mangel an semantischen Informationen innerhalb von Punkt-Ensembles auf technische Grenzen. Die Verbindung von Wissensquellen ist immer noch ein sehr manueller und zeitaufwendiger Prozess, der unter fehleranfälliger menschlicher Interpretation leidet. Dies verdeutlicht den starken Bedarf an domänenbezogenen Datenanalysen, um eine kohärente und strukturierte Information zu schaffen. Die Arbeit versucht eindeutig, Automatisierungsprobleme in der Punktwolkenverarbeitung zu lösen, um intelligente Umgebungen zu schaffen, d.h. virtuelle Kopien, die in vollständig autonome Argumentationsdienste verwendet/integriert werden können. Wir befassen uns mit Punktwolkenfragen im Zusammenhang mit der Wissensextraktion - insbesondere Segmentierung und Klassifizierung - Strukturierung, Visualisierung und Interaktion mit kognitiven Entscheidungssystemen. Wir schlagen vor, sowohl Punktwolkeneigenschaften als auch formalisiertes Wissen zu verbinden, um schnell relevante Informationen mithilfe von domänenzentrierten Grafiken zu extrahieren. Die Dissertation liefert das Konzept einer Smart Point Cloud (SPC) Infrastruktur, die als interoperable und modulare Architektur für eine einheitliche Verarbeitung dient. Es ermöglicht eine einfache Integration in bestehende Workflows und eine multidimensionale Spezialisierung durch Gerätewissen, analytisches Wissen oder Domänenwissen. Konzepte, Algorithmen, Code und Materialien werden zur Verfügung gestellt, um Erkenntnisse zu replizieren und aktuelle Anwendungen zu erweitern

Optimising mobile laser scanning for underground mines

Despite several technological advancements, underground mines are still largely relied on visual inspections or discretely placed direct-contact measurement sensors for routine monitoring. Such approaches are manual and often yield inconclusive, unreliable and unscalable results besides exposing mine personnel to field hazards. Mobile laser scanning (MLS) promises an automated approach that can generate comprehensive information by accurately capturing large-scale 3D data. Currently, the application of MLS has relatively remained limited in mining due to challenges in the post-registration of scans and the unavailability of suitable processing algorithms to provide a fully automated mapping solution. Additionally, constraints such as the absence of a spatial positioning network and the deficiency of distinguishable features in underground mining spaces pose challenges in mobile mapping. This thesis aims to address these challenges in mine inspections by optimising different aspects of MLS: (1) collection of large-scale registered point cloud scans of underground environments, (2) geological mapping of structural discontinuities, and (3) inspection of structural support features. Firstly, a spatial positioning network was designed using novel three-dimensional unique identifiers (3DUID) tags and a 3D registration workflow (3DReG), to accurately obtain georeferenced and coregistered point cloud scans, enabling multi-temporal mapping. Secondly, two fully automated methods were developed for mapping structural discontinuities from point cloud scans – clustering on local point descriptors (CLPD) and amplitude and phase decomposition (APD). These methods were tested on both surface and underground rock mass for discontinuity characterisation and kinematic analysis of the failure types. The developed algorithms significantly outperformed existing approaches, including the conventional method of compass and tape measurements. Finally, different machine learning approaches were used to automate the recognition of structural support features, i.e. roof bolts from point clouds, in a computationally efficient manner. Roof bolts being mapped from a scanned point cloud provided an insight into their installation pattern, which underpinned the applicability of laser scanning to inspect roof supports rapidly. Overall, the outcomes of this study lead to reduced human involvement in field assessments of underground mines using MLS, demonstrating its potential for routine multi-temporal monitoring

Remote Sensing and Geosciences for Archaeology

This book collects more than 20 papers, written by renowned experts and scientists from across the globe, that showcase the state-of-the-art and forefront research in archaeological remote sensing and the use of geoscientific techniques to investigate archaeological records and cultural heritage. Very high resolution satellite images from optical and radar space-borne sensors, airborne multi-spectral images, ground penetrating radar, terrestrial laser scanning, 3D modelling, Geographyc Information Systems (GIS) are among the techniques used in the archaeological studies published in this book. The reader can learn how to use these instruments and sensors, also in combination, to investigate cultural landscapes, discover new sites, reconstruct paleo-landscapes, augment the knowledge of monuments, and assess the condition of heritage at risk. Case studies scattered across Europe, Asia and America are presented: from the World UNESCO World Heritage Site of Lines and Geoglyphs of Nasca and Palpa to heritage under threat in the Middle East and North Africa, from coastal heritage in the intertidal flats of the German North Sea to Early and Neolithic settlements in Thessaly. Beginners will learn robust research methodologies and take inspiration; mature scholars will for sure derive inputs for new research and applications

8th. International congress on archaeology computer graphica. Cultural heritage and innovation

El lema del Congreso es: 'Documentación 3D avanzada, modelado y reconstrucción de objetos patrimoniales, monumentos y sitios.Invitamos a investigadores, profesores, arqueólogos, arquitectos, ingenieros, historiadores de arte... que se ocupan del patrimonio cultural desde la arqueología, la informática gráfica y la geomática, a compartir conocimientos y experiencias en el campo de la Arqueología Virtual. La participación de investigadores y empresas de prestigio será muy apreciada. Se ha preparado un atractivo e interesante programa para participantes y visitantes.Lerma García, JL. (2016). 8th. International congress on archaeology computer graphica. Cultural heritage and innovation. Editorial Universitat Politècnica de València. http://hdl.handle.net/10251/73708EDITORIA