The Method for Generating a Set of Reference Images for Assessing the Condition of Critical Infrastructure Facilities Using Mobile Robots

The purpose of this work is to improve the accuracy of critical infrastructure condition assessment using mobile robots by considering the geometric distortions of the current images during the formation of a set of reference images. The goal is achieved by determining the sampling step values by angles and sighting height without loss of accuracy. The most important result is the determination of acceptable discretization values in the range of angles and heights of a correlation-extreme navigation system. The significance of the obtained results is in solving the problem of forming a set of reference images, which will reduce the impact of changes in the geometry of sighting on the accuracy of the evaluation of objects. A special feature of the results obtained is the establishment of maximum permissible sampling steps in angles and heights of sight to ensure the required accuracy of object state estimation. When forming a set of reference images, the sampling step by height should be (0.06....0.11)% and (0.12....0.2)% relative to the initial flight altitude for the sighting surface with normal and high object saturation, respectively. The angular sampling step is 10...17 degrees and 6...10 degrees, respectively, for the same surface types. The difference from known works is that the perspective and scale distortions are considered at the stage of formation of a set of reference images, which ensures high accuracy of the system functioning in conditions of orientation and sighting geometry changes

Reconstruction of Specular Reflective Surfaces using Auto-Calibrating Deflectometry

This thesis discusses deflectometry as a reconstruction method for highly reflecting surfaces. It focuses on deflectometry alone and does not use other reconstruction techniques to supplement with additional data. It explains the measurement process and principle and provides a crash course into an efficient mathematical representation of the principles involved. Using this, it reformulates existing three-dimensional reconstructing methods, expands upon them and develops new ones

Reconstruction of Specular Reflective Surfaces using Auto-Calibrating Deflectometry

This thesis discusses deflectometry as a reconstruction method for highly reflecting surfaces. It focuses on deflectometry alone and does not use other reconstruction techniques to supplement with additional data. It explains the measurement process and principle and provides a crash course into an efficient mathematical representation of the principles involved. Using this, it reformulates existing three-dimensional reconstructing methods, expands upon them and develops new ones

Proceedings of the First International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2014): Porto, Portugal

Proceedings of: First International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2014). Porto (Portugal), August 27-28, 2014

An Innovative Solution to NASA's NEO Impact Threat Mitigation Grand Challenge and Flight Validation Mission Architecture Development

This final technical report describes the results of a NASA Innovative Advanced Concept (NIAC) Phase 2 study entitled "An Innovative Solution to NASA's NEO Impact Threat Mitigation Grand Challenge and Flight Validation Mission Architecture Development." This NIAC Phase 2 study was conducted at the Asteroid Deflection Research Center (ADRC) of Iowa State University in 2012-2014. The study objective was to develop an innovative yet practically implementable solution to the most probable impact threat of an asteroid or comet with short warning time (less than 5 years). The technical materials contained in this final report are based on numerous technical papers, which have been previously published by the project team of the NIAC Phase 1 and 2 studies during the past three years. Those technical papers as well as a NIAC Phase 2 Executive Summary report can be downloaded from the ADRC website (www.adrc.iastate.edu)

Rekonstruktion, Analyse und Editierung dynamisch deformierter 3D-Oberflächen

Dynamically deforming 3D surfaces play a major role in computer graphics. However, producing time-varying dynamic geometry at ever increasing detail is a time-consuming and costly process, and so a recent trend is to capture geometry data directly from the real world. In the first part of this thesis, I propose novel approaches for this research area. These approaches capture dense dynamic 3D surfaces from multi-camera systems in a particularly robust and accurate way. This provides highly realistic dynamic surface models for phenomena like moving garments and bulging muscles. However, re-using, editing, or otherwise analyzing dynamic 3D surface data is not yet conveniently possible. To close this gap, the second part of this dissertation develops novel data-driven modeling and animation approaches. I first show a supervised data-driven approach for modeling human muscle deformations that scales to huge datasets and provides fine-scale, anatomically realistic deformations at high quality not attainable by previous methods. I then extend data-driven modeling to the unsupervised case, providing editing tools for a wider set of input data ranging from facial performance capture and full-body motion to muscle and cloth deformation. To this end, I introduce the concepts of sparsity and locality within a mathematical optimization framework. I also explore these concepts for constructing shape-aware functions that are useful for static geometry processing, registration, and localized editing.Dynamisch deformierbare 3D-Oberflächen spielen in der Computergrafik eine zentrale Rolle. Die Erstellung der für Computergrafik-Anwendungen benötigten, hochaufgelösten und zeitlich veränderlichen Oberflächengeometrien ist allerdings äußerst arbeitsintensiv. Aus dieser Problematik heraus hat sich der Trend entwickelt, Oberflächendaten direkt aus Aufnahmen der echten Welt zu erfassen. Dazu nötige 3D-Rekonstruktionsverfahren werden im ersten Teil der Arbeit entwickelt. Die vorgestellten, neuartigen Verfahren erlauben die Erfassung dynamischer 3D-Oberflächen aus Mehrkamera-Aufnahmen bei hoher Verlässlichkeit und Präzision. Auf diese Weise können detaillierte Oberflächenmodelle von Phänomenen wie in Bewegung befindliche Kleidung oder sich anspannende Muskeln erfasst werden. Aber auch die Wiederverwendung, Bearbeitung und Analyse derlei gewonnener 3D-Oberflächendaten ist aktuell noch nicht auf eine einfache Art und Weise möglich. Um diese Lücke zu schließen beschäftigt sich der zweite Teil der Arbeit mit der datengetriebenen Modellierung und Animation. Zunächst wird ein Ansatz für das überwachte Lernen menschlicher Muskel-Deformationen vorgestellt. Dieses neuartige Verfahren ermöglicht eine datengetriebene Modellierung mit besonders umfangreichen Datensätzen und liefert anatomisch-realistische Deformationseffekte. Es übertrifft damit die Genauigkeit früherer Methoden. Im nächsten Teil beschäftigt sich die Dissertation mit dem unüberwachten Lernen aus 3D-Oberflächendaten. Es werden neuartige Werkzeuge vorgestellt, die eine weitreichende Menge an Eingabedaten verarbeiten können, von aufgenommenen Gesichtsanimationen über Ganzkörperbewegungen bis hin zu Muskel- und Kleidungsdeformationen. Um diese Anwendungsbreite zu erreichen stützt sich die Arbeit auf die allgemeinen Konzepte der Spärlichkeit und Lokalität und bettet diese in einen mathematischen Optimierungsansatz ein. Abschließend zeigt die vorliegende Arbeit, wie diese Konzepte auch für die Konstruktion von oberflächen-adaptiven Basisfunktionen übertragen werden können. Dadurch können Anwendungen für die Verarbeitung, Registrierung und Bearbeitung statischer Oberflächenmodelle erschlossen werden

Analysis of 3D objects at multiple scales (application to shape matching)

Depuis quelques années, l évolution des techniques d acquisition a entraîné une généralisation de l utilisation d objets 3D très dense, représentés par des nuages de points de plusieurs millions de sommets. Au vu de la complexité de ces données, il est souvent nécessaire de les analyser pour en extraire les structures les plus pertinentes, potentiellement définies à plusieurs échelles. Parmi les nombreuses méthodes traditionnellement utilisées pour analyser des signaux numériques, l analyse dite scale-space est aujourd hui un standard pour l étude des courbes et des images. Cependant, son adaptation aux données 3D pose des problèmes d instabilité et nécessite une information de connectivité, qui n est pas directement définie dans les cas des nuages de points. Dans cette thèse, nous présentons une suite d outils mathématiques pour l analyse des objets 3D, sous le nom de Growing Least Squares (GLS). Nous proposons de représenter la géométrie décrite par un nuage de points via une primitive du second ordre ajustée par une minimisation aux moindres carrés, et cela à pour plusieurs échelles. Cette description est ensuite derivée analytiquement pour extraire de manière continue les structures les plus pertinentes à la fois en espace et en échelle. Nous montrons par plusieurs exemples et comparaisons que cette représentation et les outils associés définissent une solution efficace pour l analyse des nuages de points à plusieurs échelles. Un défi intéressant est l analyse d objets 3D acquis dans le cadre de l étude du patrimoine culturel. Dans cette thèse, nous nous étudions les données générées par l acquisition des fragments des statues entourant par le passé le Phare d Alexandrie, Septième Merveille du Monde. Plus précisément, nous nous intéressons au réassemblage d objets fracturés en peu de fragments (une dizaine), mais avec de nombreuses parties manquantes ou fortement dégradées par l action du temps. Nous proposons un formalisme pour la conception de systèmes d assemblage virtuel semi-automatiques, permettant de combiner à la fois les connaissances des archéologues et la précision des algorithmes d assemblage. Nous présentons deux systèmes basés sur cette conception, et nous montrons leur efficacité dans des cas concrets.Over the last decades, the evolution of acquisition techniques yields the generalization of detailed 3D objects, represented as huge point sets composed of millions of vertices. The complexity of the involved data often requires to analyze them for the extraction and characterization of pertinent structures, which are potentially defined at multiple scales. Amongthe wide variety of methods proposed to analyze digital signals, the scale-space analysis istoday a standard for the study of 2D curves and images. However, its adaptation to 3D dataleads to instabilities and requires connectivity information, which is not directly availablewhen dealing with point sets.In this thesis, we present a new multi-scale analysis framework that we call the GrowingLeast Squares (GLS). It consists of a robust local geometric descriptor that can be evaluatedon point sets at multiple scales using an efficient second-order fitting procedure. We proposeto analytically differentiate this descriptor to extract continuously the pertinent structuresin scale-space. We show that this representation and the associated toolbox define an effi-cient way to analyze 3D objects represented as point sets at multiple scales. To this end, we demonstrate its relevance in various application scenarios.A challenging application is the analysis of acquired 3D objects coming from the CulturalHeritage field. In this thesis, we study a real-world dataset composed of the fragments ofthe statues that were surrounding the legendary Alexandria Lighthouse. In particular, wefocus on the problem of fractured object reassembly, consisting of few fragments (up to aboutten), but with missing parts due to erosion or deterioration. We propose a semi-automaticformalism to combine both the archaeologist s knowledge and the accuracy of geometricmatching algorithms during the reassembly process. We use it to design two systems, andwe show their efficiency in concrete cases.BORDEAUX1-Bib.electronique (335229901) / SudocSudocFranceF

Analysis of 3D objects at multiple scales (application to shape matching)

Depuis quelques années, l évolution des techniques d acquisition a entraîné une généralisation de l utilisation d objets 3D très dense, représentés par des nuages de points de plusieurs millions de sommets. Au vu de la complexité de ces données, il est souvent nécessaire de les analyser pour en extraire les structures les plus pertinentes, potentiellement définies à plusieurs échelles. Parmi les nombreuses méthodes traditionnellement utilisées pour analyser des signaux numériques, l analyse dite scale-space est aujourd hui un standard pour l étude des courbes et des images. Cependant, son adaptation aux données 3D pose des problèmes d instabilité et nécessite une information de connectivité, qui n est pas directement définie dans les cas des nuages de points. Dans cette thèse, nous présentons une suite d outils mathématiques pour l analyse des objets 3D, sous le nom de Growing Least Squares (GLS). Nous proposons de représenter la géométrie décrite par un nuage de points via une primitive du second ordre ajustée par une minimisation aux moindres carrés, et cela à pour plusieurs échelles. Cette description est ensuite derivée analytiquement pour extraire de manière continue les structures les plus pertinentes à la fois en espace et en échelle. Nous montrons par plusieurs exemples et comparaisons que cette représentation et les outils associés définissent une solution efficace pour l analyse des nuages de points à plusieurs échelles. Un défi intéressant est l analyse d objets 3D acquis dans le cadre de l étude du patrimoine culturel. Dans cette thèse, nous nous étudions les données générées par l acquisition des fragments des statues entourant par le passé le Phare d Alexandrie, Septième Merveille du Monde. Plus précisément, nous nous intéressons au réassemblage d objets fracturés en peu de fragments (une dizaine), mais avec de nombreuses parties manquantes ou fortement dégradées par l action du temps. Nous proposons un formalisme pour la conception de systèmes d assemblage virtuel semi-automatiques, permettant de combiner à la fois les connaissances des archéologues et la précision des algorithmes d assemblage. Nous présentons deux systèmes basés sur cette conception, et nous montrons leur efficacité dans des cas concrets.Over the last decades, the evolution of acquisition techniques yields the generalization of detailed 3D objects, represented as huge point sets composed of millions of vertices. The complexity of the involved data often requires to analyze them for the extraction and characterization of pertinent structures, which are potentially defined at multiple scales. Amongthe wide variety of methods proposed to analyze digital signals, the scale-space analysis istoday a standard for the study of 2D curves and images. However, its adaptation to 3D dataleads to instabilities and requires connectivity information, which is not directly availablewhen dealing with point sets.In this thesis, we present a new multi-scale analysis framework that we call the GrowingLeast Squares (GLS). It consists of a robust local geometric descriptor that can be evaluatedon point sets at multiple scales using an efficient second-order fitting procedure. We proposeto analytically differentiate this descriptor to extract continuously the pertinent structuresin scale-space. We show that this representation and the associated toolbox define an effi-cient way to analyze 3D objects represented as point sets at multiple scales. To this end, we demonstrate its relevance in various application scenarios.A challenging application is the analysis of acquired 3D objects coming from the CulturalHeritage field. In this thesis, we study a real-world dataset composed of the fragments ofthe statues that were surrounding the legendary Alexandria Lighthouse. In particular, wefocus on the problem of fractured object reassembly, consisting of few fragments (up to aboutten), but with missing parts due to erosion or deterioration. We propose a semi-automaticformalism to combine both the archaeologist s knowledge and the accuracy of geometricmatching algorithms during the reassembly process. We use it to design two systems, andwe show their efficiency in concrete cases.BORDEAUX1-Bib.electronique (335229901) / SudocSudocFranceF

Multimodal Navigation for Accurate Space Rendezvous Missions

© Cranfield University 2021. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright ownerRelative navigation is paramount in space missions that involve rendezvousing between two spacecraft. It demands accurate and continuous estimation of the six degree-of-freedom relative pose, as this stage involves close-proximity-fast-reaction operations that can last up to five orbits. This has been routinely achieved thanks to active sensors such as lidar, but their large size, cost, power and limited operational range remain a stumbling block for en masse on-board integration. With the onset of faster processing units, lighter and cheaper passive optical sensors are emerging as the suitable alternative for autonomous rendezvous in combination with computer vision algorithms. Current vision-based solutions, however, are limited by adverse illumination conditions such as solar glare, shadowing, and eclipse. These effects are exacerbated when the target does not hold cooperative markers to accommodate the estimation process and is incapable of controlling its rotational state. This thesis explores novel model-based methods that exploit sequences of monoc ular images acquired by an on-board camera to accurately carry out spacecraft relative pose estimation for non-cooperative close-range rendezvous with a known artificial target. The proposed solutions tackle the current challenges of imaging in the visible spectrum and investigate the contribution of the long wavelength infrared (or “thermal”) band towards a combined multimodal approach. As part of the research, a visible-thermal synthetic dataset of a rendezvous approach with the defunct satellite Envisat is generated from the ground up using a realistic orbital camera simulator. From the rendered trajectories, the performance of several state-of-the-art feature detectors and descriptors is first evaluated for both modalities in a tailored scenario for short and wide baseline image processing transforms. Multiple combinations, including the pairing of algorithms with their non-native counterparts, are tested. Computational runtimes are assessed in an embedded hardware board. From the insight gained, a method to estimate the pose on the visible band is derived from minimising geometric constraints between online local point and edge contour features matched to keyframes generated offline from a 3D model of the target. The combination of both feature types is demonstrated to achieve a pose solution for a tumbling target using a sparse set of training images, bypassing the need for hardware-accelerated real-time renderings of the model. The proposed algorithm is then augmented with an extended Kalman filter which processes each feature-induced minimisation output as individual pseudo measurements, fusing them to estimate the relative pose and velocity states at each time-step. Both the minimisation and filtering are established using Lie group formalisms, allowing for the covariance of the solution computed by the former to be automatically incorporated as measurement noise in the latter, providing an automatic weighing of each feature type directly related to the quality of the matches. The predicted states are then used to search for new feature matches in the subsequent time-step. Furthermore, a method to derive a coarse viewpoint estimate to initialise the nominal algorithm is developed based on probabilistic modelling of the target’s shape. The robustness of the complete approach is demonstrated for several synthetic and laboratory test cases involving two types of target undergoing extreme illumination conditions. Lastly, an innovative deep learning-based framework is developed by processing the features extracted by a convolutional front-end with long short-term memory cells, thus proposing the first deep recurrent convolutional neural network for spacecraft pose estimation. The framework is used to compare the performance achieved by visible-only and multimodal input sequences, where the addition of the thermal band is shown to greatly improve the performance during sunlit sequences. Potential limitations of this modality are also identified, such as when the target’s thermal signature is comparable to Earth’s during eclipse.PH