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Cartographie sémantique hybride de scènes urbaines à partir de données image et Lidar / Mohamed Boussaha (2020)
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Titre : Cartographie sémantique hybride de scènes urbaines à partir de données image et Lidar Titre original : 3D hybrid urban scene semantic mapping from multi-modal data Type de document : Thèse/HDR Auteurs : Mohamed Boussaha , Auteur ; Bruno Vallet
, Directeur de thèse ; Patrick Rives, Directeur de thèse
Editeur : Champs/Marne : Université Paris-Est Année de publication : 2020 Projets : PLaTINUM / Gouet-Brunet, Valérie Note générale : bibliographie
Dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy delivered by Université Paris-EstLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] apprentissage dirigé
[Termes IGN] carte de profondeur
[Termes IGN] descripteur
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] fusion de données multisource
[Termes IGN] image panoramique
[Termes IGN] maillage par triangles
[Termes IGN] reconstruction 3D du bâti
[Termes IGN] réflectance
[Termes IGN] scène intérieure
[Termes IGN] scène urbaine
[Termes IGN] segmentation sémantique
[Termes IGN] semis de points
[Termes IGN] système de numérisation mobile
[Termes IGN] texturage
[Termes IGN] traitement de semis de pointsIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Avec la démocratisation des applications collaboratives d'assistance à la navigation et l'avènement de robots autonomes, la cartographie mobile suscite ces dernières années une attention croissante, tant dans les milieux académiques qu'industriels. La numérisation de l'environnement offre non seulement une connaissance fine et exhaustive permettant aux usagers d'anticiper et de planifier leurs déplacements, mais garantit aussi la disponibilité d'informations fiables notamment en cas d'éventuelle défaillance des capteurs visuels d'un véhicule autonome. S'agissant d'un enjeu crucial pour une navigation fiable, la cartographie mobile soulève en revanche de nombreux défis en matière de robustesse, de précision et de passage à l'échelle. Cette problématique fait appel à des méthodes qui requièrent une capacité de traitement de données massives avec une précision centimétrique tout en gérant les spécificités de l'acquisition (la variabilité du niveau de détails, des occultations et des fortes variations de luminosité).
Cette thèse porte sur le développement d'un référentiel global géolocalisé de l'environnement urbain constitué de représentations 3D géométriques, photométriques et sémantiques. Dans un premier temps, une investigation approfondie de la représentation la plus adaptée à un tel référentiel, permet une reconstruction d'une carte haute définition à large échelle sous forme d'un maillage 3D texturé. Cette représentation est mise en place par fusion multimodale d'images orientées et de balayages LiDAR géo-référencés acquis depuis une plateforme de cartographie mobile terrestre. Par la suite, nous proposons d'intégrer l'aspect sémantique au référentiel 3D reconstruit en exploitant la complémentarité entre les modalités d'acquisition photométriques et géométriques. À travers la riche littérature sur le sujet, nous identifions l'absence d'un jeu de données urbain multimodal annoté incluant un maillage texturé à large échelle. Nous abordons ce verrou par la production d'un jeu de données composé de nuages de point 3D, d'images 2D perspectives et panoramiques, de cartes de profondeur et de reflectance ainsi qu'un maillage texturé avec les annotations correspondantes à chaque modalité. Dans un second temps, nous considérons le référentiel comme un nuage de points structuré par un graphe d'adjacence. Nous introduisons une nouvelle approche de sur-segmentation par apprentissage supervisé. Cette méthode opère en deux temps: calcul de descripteurs locaux des points 3D par apprentissage profond de métrique, puis partition du nuage de points en zones uniformes, appelées superpoints. Les descripteurs sont appris de telle sorte qu'ils présentent de forts contrastes à l'interface entre objets, incitant la partition résultante à suivre leurs contours naturels. Nos expériences sur des scènes intérieures et extérieures montrent la nette supériorité de notre approche sur les méthodes de partition de nuage de points de l'état de l'art, qui ne reposaient pas jusqu'à là sur l'apprentissage machine. Nous montrons également que notre méthode peut être combinée à un algorithme de classification de superpoints pour obtenir d'excellents résultats en terme de segmentation sémantique, améliorant aussi l'état de l'art sur ce sujet. Enfin, nous étendons cette approche aux maillages texturés. Les triangles, structurés cette fois-ci par le graphe d'adjacence du maillage, sont partitionnés en groupes homogènes appelés superfacettes. À l'instar des nuages de points, des descripteurs locaux du maillage texturé sont appris de façon à ce que les frontières d'objets sémantiquement distincts présentent un contraste élevé. Ces descripteurs sont le résultat d'une fusion des descripteurs appris sur le maillage par convolution des arêtes d'une part, et des descripteurs de texture d'autre part. Les expériences réalisées sur notre jeu de données illustrent la supériorité de notre approche par rapport aux méthodes de l'état de l'art de sur-segmentation de maillage.Numéro de notice : 17674 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de doctorat : Geographical Information Sciences and technologies : UPE : 2020 Organisme de stage : LaSTIG (IGN) nature-HAL : Thèse En ligne : https://hal.science/tel-03276242v1 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98009
Titre : Collaborative visual-inertial state and scene estimation Type de document : Thèse/HDR Auteurs : Marco Karrer, Auteur ; Margarita Chli, Directeur de thèse Editeur : Zurich : Eidgenossische Technische Hochschule ETH - Ecole Polytechnique Fédérale de Zurich EPFZ Année de publication : 2020 Importance : 151 p. Format : 21 x 30 cm Note générale : bibliographie
A thesis submitted to attain the degree of Doctor of Sciences of ETH Zurich in Mechanical EngineeringLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s)
[Termes IGN] cartographie et localisation simultanées
[Termes IGN] centrale inertielle
[Termes IGN] compensation par faisceaux
[Termes IGN] estimation de pose
[Termes IGN] image captée par drone
[Termes IGN] reconstruction d'objet
[Termes IGN] robotique
[Termes IGN] système multi-agents
[Termes IGN] vision par ordinateurIndex. décimale : THESE Thèses et HDR Résumé : (auteur) The capability of a robot to create a map of its workspace on the fly, while constantly updating it and continuously estimating its motion in it, constitutes one of the central research problems in mobile robotics and is referred to as Simultaneous Localization And Mapping (SLAM) in the literature. Relying solely on the sensor-suite onboard the robot, SLAM is a core building block in enabling the navigational autonomy necessary to facilitate the general use of mobile robots and has been the subject of booming research interest spanning over three decades. With the largest body of related literature addressing the challenge of single-agent SLAM, it is only very recently, with the relative maturity of this field that approaches tackling collaborative SLAM with multiple agents have started appearing. The potential of collaborative multi-agent SLAM is great; not only promising to boost the efficiency of robotic missions by splitting the task at hand to more agents but also to improve the overall robustness and accuracy by boosting the amount of data that each agent’s estimation process has access to. While SLAM can be performed using a variety of different sensors, this thesis is focused on the fusion of visual and inertial cues, as one of the most common combinations of sensing modalities in robotics today. The information richness captured by cameras, along with the high-frequency and metric information provided by Inertial Measurement Units (IMUs) in combination with the low weight and power consumption offered by a visual-inertial sensor suite render this setup ideal for a wide variety of applications and robotic platforms, in particular to resource-constrained platforms such as Unmanned Aerial Vehicles (UAVs). The majority of the state-of-the-art visual-inertial estimators are designed as odometry algorithms, providing only estimates consistent within a limited time-horizon. This lack in global consistency of estimates, however, poses a major hurdle in an effective fusion of data from multiple agents and the practi- cal definition of a common reference frame, which is imperative before collaborative effort can be coordinated. In this spirit, this thesis investigates the potential of global optimization, based on a central access point (server) as a first approach, demonstrating global consistency using only monocular-inertial data. Fusing data from multiple agents, not only consistency can be maintained, but also the accuracy is shown to improve at times, revealing the great potential of collaborative SLAM. Aiming at improving the computational efficiency, in a second approach a more efficient system architecture is employed, allowing a more suitable distribution of the computational load amongst the agents and the server. Furthermore, the architecture implements a two-way communication enabling a tighter collaboration between the agents as they become capable of re-using information captured by other agents through communication with the server, enabling improvements of their onboard pose tracking online, during the mission. In addition to general collaborative SLAM without specific assumptions on the agents’ relative pose configuration, we investigate the potential of a configuration with two agents, carrying one camera each with overlapping fields of view, essentially forming a virtual stereo camera. With the ability of each robotic agent to move independently, the potential to control the stereo baseline according to the scene depth is very promising, for example at high altitudes where all scene points are far away and, therefore, only provide weak constraints on the metric scale in a standard single-agent system. To this end, an approach to estimate the time-varying stereo transformation formed between two agents is proposed, by fusing the egomotion estimates of the individual agents along with the image measurements extracted from the view-overlap in a tightly coupled fashion. Taking this virtual stereo camera idea a step further, a novel collaboration framework is presented, utilizing the view-overlap along with relative distance measurements across the two agents (e.g. obtained via Ultra-Wide Band (UWB) modules), in order to successfully perform state estimation at high altitudes where state-of-the-art single-agent methods fail. In the interest of low-latency pose estimation, each agent holds its own estimate of the map, while consistency between the agents is achieved using a novel consensus-based sliding window bundle adjustment. Despite that in this work, experiments are shown in a two-agent setup, the proposed distributed bundle adjustment scheme holds great potential for scaling up to larger problems with multiple agents, due to the asynchronicity of the proposed estimation process and the high level of parallelism it permits. The majority of the developed approaches in this thesis rely on sparse feature maps in order to allow for efficient and timely pose estimation, however, this translates to reduced awareness of the spatial structure of a robot’s workspace, which can be insufficient for tasks requiring careful scene interaction and manipulation of objects. Equipping a typical visual-inertial sensor suite with an RGB-D camera, an add-on framework is presented that enables the efficient fusion of naturally noisy depth information into an accurate, local, dense map of the scene, providing sufficient information for an agent to plan contact with a surface. With the focus on collaborative SLAM using visual-inertial data, the approaches and systems presented in this thesis contribute towards achieving collaborative Visual-Inertial SLAM (VI-SLAM) deployable in challenging real-world scenarios, where the participating agents’ experiences get fused and processed at a central access point. On the other side, it is shown that taking advantage of specific configurations can push the collaboration amongst the agents towards achieving greater general robustness and accuracy of scene and egomotion estimates in scenarios, where state-of-the-art single-agent systems are otherwise unsuccessful, paving the way towards intelligent robot collaboration. Note de contenu : Introduction
1- Real-time dense surface reconstruction for aerial manipulation
2- Towards globally consistent visual-inertial collaborative SLAM
3- CVI-SLAM – collaborative visual-inertial SLAM
4- Collaborative 6DoF relative pose estimation for two UAVs with overlapping fields of view
5- Distributed variable-baseline stereo SLAM from two UAVsNuméro de notice : 28318 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse étrangère Note de thèse : PhD Thesis : Mechanical Engineering : ETH Zurich : 2020 DOI : sans En ligne : https://www.research-collection.ethz.ch/handle/20.500.11850/465334 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98251 Comparison of multi-seasonal Landsat 8, Sentinel-2 and hyperspectral images for mapping forest alliances in Northern California / Matthew L. Clark in ISPRS Journal of photogrammetry and remote sensing, vol 159 (January 2020)
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[article]
Titre : Comparison of multi-seasonal Landsat 8, Sentinel-2 and hyperspectral images for mapping forest alliances in Northern California Type de document : Article/Communication Auteurs : Matthew L. Clark, Auteur Année de publication : 2020 Article en page(s) : pp 26 - 40 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse comparative
[Termes IGN] apprentissage automatique
[Termes IGN] Californie (Etats-Unis)
[Termes IGN] carte forestière
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] couvert végétal
[Termes IGN] image AVIRIS
[Termes IGN] image hyperspectrale
[Termes IGN] image Landsat-8
[Termes IGN] image Sentinel-MSI
[Termes IGN] occupation du sol
[Termes IGN] Short Waves InfraRedRésumé : (Auteur) The current era of earth observation now provides constellations of open-access, multispectral satellite imagery with medium spatial resolution, greatly increasing the frequency of cloud-free data for analysis. The Landsat satellites have a long historical record, while the newer Sentinel-2 (S2) satellites offer higher temporal, spatial and spectral resolution. The goal of this study was to evaluate the relative benefits of single- and multi-seasonal multispectral satellite data for discriminating detailed forest alliances, as defined by the U.S. National Vegetation Classification system, in a Mediterranean-climate landscape (Sonoma County, California). Results were compared to a companion analysis of simulated hyperspectral satellite data (HyspIRI) for the same study site and reference data (Clark et al., 2018). Experiments used real and simulated S2 and Landsat 8 (L8) data. Simulated S2 and L8 were from HyspIRI images, thereby focusing results on differences in spectral resolution rather than other confounding factors. The Support Vector Machine (SVM) classifier was used in a hierarchical classification of land-cover (Level 1), followed by alliances (Level 2) in forest pixels, and included summer-only and multi-seasonal sets of predictor variables (bands, indices and bands plus indices). Both real and simulated multi-seasonal multispectral variables significantly improved overall accuracy (OA) by 0.2–1.6% for Level 1 tree/no tree classifications and 3.6–25.8% for Level 2 forest alliances. Classifiers with S2 variables tended to be more accurate than L8 variables, particularly for S2, which had 0.4–2.1% and 5.1–11.8% significantly higher OA than L8 for Level 1 tree/no tree and Level 2 forest alliances, respectively. Combining multispectral bands and indices or using just bands was generally more accurate than relying on just indices for classification. Simulated HyspIRI variables from past research had significantly greater accuracy than real L8 and S2 variables, with an average OA increase of 8.2–12.6%. A final alliance-level map used for a deeper analysis used simulated multi-seasonal S2 bands and indices, which had an overall accuracy of 74.3% (Kappa = 0.70). The accuracy of this classification was only 1.6% significantly lower than the best HyspIRI-based classification, which used multi-seasonal metrics (Clark et al., 2018), and there were alliances where the S2-based classifier was more accurate. Within the context of these analyses and study area, S2 spectral-temporal data demonstrated a strong capability for mapping global forest alliances, or similar detailed floristic associations, at medium spatial resolutions (10–30 m). Numéro de notice : A2020-011 Affiliation des auteurs : non IGN Thématique : FORET/GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.11.007 Date de publication en ligne : 14/11/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.11.007 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94399
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Titre : Convolutional Neural Networks for embedded vision Titre original : Réseaux de neurones CNN pour la vision embarquée Type de document : Thèse/HDR Auteurs : Lucas Fernandez Brillet, Auteur ; Stéphane Mancini, Directeur de thèse Editeur : Grenoble [France] : Université Grenoble Alpes Année de publication : 2020 Importance : 164 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse pour obtenir le grade de Docteur de l'Université Grenoble Alpes, Spécialité : Mathématiques, sciences et technologies de
l’information, informatiqueLangues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse en composantes principales
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] compression d'image
[Termes IGN] détection d'objet
[Termes IGN] image à haute résolution
[Termes IGN] instrument embarqué
[Termes IGN] vision par ordinateur
[Termes IGN] zone d'intérêtIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Recently, Convolutional Neural Networks have become the state-of-the-art soluion(SOA) to most computer vision problems. In order to achieve high accuracy rates, CNNs require a high parameter count, as well as a high number of operations. This greatly complicates the deployment of such solutions in embedded systems, which strive to reduce memory size. Indeed, while most embedded systems are typically in the range of a few KBytes of memory, CNN models from the SOA usually account for multiple MBytes, or even GBytes in model size. Throughout this thesis, multiple novel ideas allowing to ease this issue are proposed. This requires to jointly design the solution across three main axes: Application, Algorithm and Hardware.In this manuscript, the main levers allowing to tailor computational complexity of a generic CNN-based object detector are identified and studied. Since object detection requires scanning every possible location and scale across an image through a fixed-input CNN classifier, the number of operations quickly grows for high-resolution images. In order to perform object detection in an efficient way, the detection process is divided into two stages. The first stage involves a region proposal network which allows to trade-off recall for the number of operations required to perform the search, as well as the number of regions passed on to the next stage. Techniques such as bounding box regression also greatly help reduce the dimension of the search space. This in turn simplifies the second stage, since it allows to reduce the task’s complexity to the set of possible proposals. Therefore, parameter counts can greatly be reduced.Furthermore, CNNs also exhibit properties that confirm their over-dimensionment. This over-dimensionement is one of the key success factors of CNNs in practice, since it eases the optimization process by allowing a large set of equivalent solutions. However, this also greatly increases computational complexity, and therefore complicates deploying the inference stage of these algorithms on embedded systems. In order to ease this problem, we propose a CNN compression method which is based on Principal Component Analysis (PCA). PCA allows to find, for each layer of the network independently, a new representation of the set of learned filters by expressing them in a more appropriate PCA basis. This PCA basis is hierarchical, meaning that basis terms are ordered by importance, and by removing the least important basis terms, it is possible to optimally trade-off approximation error for parameter count. Through this method, it is possible to compress, for example, a ResNet-32 network by a factor of ×2 both in the number of parameters and operations with a loss of accuracy Note de contenu : Introduction
1- Deep learning overview
2- Methodology to adapt the computational complexity of CNN-based object detection for efficient inference in an applicative use-case
3- CNN compression
4- Cascaded and compressed CNNs for fast and lightweight face detection
5- Hardware evaluation on embedded multiprocessor
Thesis Conclusion & PerspectivesNuméro de notice : 28392 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Mathématiques, sciences et technologies de l’information, informatique : Grenoble : 2020 DOI : sans En ligne : https://tel.archives-ouvertes.fr/tel-03101523/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98739
Titre : Data Acquisition and Processing in Cultural Heritage Type de document : Monographie Auteurs : Gabriele Bitelli, Auteur ; Fulvio Rinaudo, Auteur ; Diego Gonzalez-Aguilera, Auteur ; Pierre Grussenmeyer, Auteur Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2020 Importance : 276 p. ISBN/ISSN/EAN : 978-3-03921-741-0 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] acquisition d'images
[Termes IGN] archéologie aérienne
[Termes IGN] image captée par drone
[Termes IGN] image multitemporelle
[Termes IGN] patrimoine culturel
[Termes IGN] reconstruction 3D
[Termes IGN] restauration virtuelle
[Termes IGN] télémètre laser terrestreRésumé : (Editeur) Advances in the knowledge of the tangible components (position, size, shape) and intangible components (identity, habits) of an historic building or site involves fundamental and complex tasks in any project related to the conservation of cultural heritage (CH). In recent years, new geotechnologies have proven their usefulness and added value to the field of cultural heritage (CH) in the tasks of recording, modeling, conserving, and visualizing. In addition, current developments in building information modeling (HBIM), allow integration and simulation of different sources of information, generating a digital twin of any complex CH construction. As a result, experts in the area have increased the number of available sensors and methodologies. However, the quick evolution of geospatial technologies makes it necessary to revise their use, integration, and application in CH. This process is difficult to adopt, due to the new options which are opened for the study, analysis, management, and valorization of CH. Therefore, the aim of the present Special Issue is to cover the latest relevant topics, trends, and best practices in geospatial technologies and processing methodologies for CH sites and scenarios as well as to introduce the new tendencies. This book originates from the Special Issue “Data Acquisition and Processing in Cultural Heritage”, focusing primarily on data and sensor integration for CH; documentation/restoration in CH; heritage 3D documentation and modeling of complex CH sites; drone inspections in CH; software development in CH; and augmented reality in CH. It is hoped that this book will provide the advice and guidance required for any CH professional, making the best possible use of these sensors and methods in CH. Numéro de notice : 26499 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-03921-741-0 En ligne : https://doi.org/10.3390/books978-3-03921-741-0 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97017 PermalinkEfficiency of updating the ionospheric models using total electron content at mid- and sub-auroral latitudes / Daria S. Kotova in GPS solutions, vol 24 n° 1 (January 2020)
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