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Autonomous navigation in complex nonplanar environments based on laser ranging / Philipp Andreas Krüsi (2016)
Titre : Autonomous navigation in complex nonplanar environments based on laser ranging Type de document : Thèse/HDR Auteurs : Philipp Andreas Krüsi, Auteur Editeur : Zurich : Eidgenossische Technische Hochschule ETH - Ecole Polytechnique Fédérale de Zurich EPFZ Année de publication : 2016 Note générale : bibliographie
A thesis submitted to attain the degree of doctor of sciences of ETH ZurichLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] algorithme ICP
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] navigation autonome
[Termes IGN] robot mobile
[Termes IGN] semis de points
[Termes IGN] télémètre laser
[Termes IGN] télémétrie laser aéroporté
[Termes IGN] télémétrie laser terrestre
[Termes IGN] temps réel
[Termes IGN] vision par ordinateurRésumé : (auteur) This thesis addresses the problem of autonomous navigation with ground robots in complex environments, which may be characterized as nonplanar and nonstatic. The goal of the presented research is to enable reliable navigation over large distances in generic indoor and outdoor environments, independent of external localization sources such as a global positioning system (GPS). Focusing on these challenges, algorithms for all building blocks of autonomous navigation—localization, mapping, terrain assessment, motion planning, and motion control—are developed, implemented, integrated, and finally evaluated in extensive field experiments. Sensor-based perception of the environment is a basic requirement for localization and mapping. We propose to use a high-frequency three-dimensional (3D) laser scanner as the main exteroceptive sensor. The advantages of this technology lie in the high density and accuracy of the provided measurements, and their independence of lighting and weather conditions. We develop a highly scalable system for six-dimensional (6D) localization and 3D mapping based on iterative closest point (ICP) matching. A topological/metric map representation, where metric information is kept in spatially constrained local submaps representing vertices in a graph, allows to build consistent large-scale maps without requiring global optimization. Long-term application in dynamic and changing environments is enabled by integrating methods for identifying dynamic objects in the scene and for continuously updating existing submaps. Planning feasible and safe motions for a robotic vehicle requires distinguishing obstacles from traversable terrain. We develop two different algorithms for terrain assessment. The first method is targeted at real-time obstacle detection in the vicinity of the robot. Assuming locally planar terrain, a grid-based obstacle map is built by analyzing the raw laser scans. The second approach is based on dense point cloud maps (which can be obtained from the ICP mapping system) and suitable for planar and nonplanar environments. The algorithm computes the geometry and the traversability of the terrain “on demand” at specific query locations, avoiding any artificial discretization or explicit surface reconstruction. The desired terrain characteristics are estimated based on statistics on the local distribution of map points. Given a specific navigation task, motion planning can be defined as the problem of reasoning about how to act based on the knowledge about the environment. This thesis addresses both local obstacle avoidance and global planning over large distances. Our approach to local planning consists of computing a set of candidate trajectories, which are shaped around nearby obstacles or along a given reference path, and enforced to satisfy the robot’s kinematic constraints. The optimal local trajectory is chosen by evaluating the motion alternatives in terms of guidance towards the goal and traversability of the underlying terrain. For global motion planning, we develop an algorithm embedding the proposed point-cloud-based terrain assessment method, which allows trajectories to be directly planned on 3D point cloud maps. The approach is designed to be suitable for generic nonplanar environments, including rough outdoor terrain, multi-level facilities, and more complex geometries. Piecewise continuous trajectories are computed in the full 6D space of robot poses, while strictly considering the vehicle’s kinematic and dynamic constraints. We apply sampling-based planning algorithms to generate an initial trajectory connecting the desired start and goal poses. Subsequently, the trajectory is locally optimized according to a generic cost function, which may include path length, path curvature, and roughness of the traversed terrain. While enforcing the hard constraints to remain satisfied (terrain contact, traversability, kinodynamic feasibility), the trajectory is iteratively deformed until a local minimum of the cost function is reached. We develop two complete systems for autonomous navigation, integrating these approaches. Combining the ICP-based localization and mapping framework with local obstacle detection and local motion planning, we implement a framework for autonomous route following, commonly referred to as teach and repeat (T&R). After a manually controlled teach run, where a graph of local submaps is built, the robot is able to automatically repeat the learned route, using the recorded maps for localization. Unlike classical T&R systems, our framework is suitable for application in dynamic environments, where the integrated obstacle avoidance scheme allows to detect and circumnavigate obstacles appearing on the reference path. In addition to the T&R approach, we present a second navigation system, integrating the point-cloud-based terrain assessment and global planning algorithms with ICP-based localization and mapping. Given a graph of point cloud maps—typically recorded in a manually controlled survey run—the framework enables navigation within the mapped area without being restricted to known routes. Motion control is implemented by a trajectory tracking controller with integrated real-time collision checking. Together with continuous map updates and frequent replanning of the global trajectory, these techniques enable autonomous navigation in nonplanar, nonstatic environments. Finally, we describe the characteristics of the mobile robot ARTOR, which was set up for the purpose of testing and evaluating the developed algorithms under realistic conditions. ARTOR consists of a six-wheeled, electrically powered base vehicle equipped with sensors, computers, and communication gear. The proposed autonomous navigation algorithms were integrated on the robot and tested in extensive field experiments, demonstrating reliable, GPS-independent navigation over large distances and under greatly varying environmental conditions, in unstructured off-road terrain, multi-level environments, and dynamic urban areas. Numéro de notice : 17367 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse étrangère Note de thèse : PhD thesis : Sciences : ETH Zurich : 2016 En ligne : http://dx.doi.org/10.3929/ethz-a-010656081 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84243 Big data, open data et valorisation des données / Jean-Louis Monino (2016)
Titre : Big data, open data et valorisation des données Type de document : Monographie Auteurs : Jean-Louis Monino, Auteur ; Soraya Sedkaoui, Auteur Editeur : Londres : ISTE Editions Année de publication : 2016 Collection : Innovation, entrepreneuriat et gestion Sous-collection : Smart innovation Importance : 158 p. Format : 15 x 23 cm ISBN/ISSN/EAN : 978-1-78405-122-8 Note générale : Bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Société de l'information
[Termes IGN] données massives
[Termes IGN] données ouvertes
[Termes IGN] entreprise
[Termes IGN] exploration de données
[Termes IGN] exploration de texte
[Termes IGN] traitement de données
[Termes IGN] valorisation des donnéesIndex. décimale : 18.30 Société de l'information Résumé : (Editeur) La série Smart innovation présente les thèmes liés à la dynamique technologique, entrepreneuriale, territoriale et économique de l'innovation. Elle privilégie les approches novatrices des acteurs qui, par leurs comportements, stratégies et politiques d'innovation intelligente, modifient les modèles économiques dans une perspective de croissance durable. Le monde est devenu numérique. Les nouvelles technologies atteignent aujourd’hui une certaine maturité, rendant les données accessibles à tous et partout. L’essor des technologies connexes, comme l’avènement d’Internet, des réseaux sociaux ou des Cloud Computing (usines numériques), accroît considérablement le volume disponible de ces données, de telle sorte que les enjeux se focalisent non plus sur leur production mais sur leur intégration et leur valorisation. Ces « Data » peuvent en effet représenter un atout majeur pour les entreprises, à condition de savoir les capter, les combiner et les analyser. Cet ouvrage analyse la révolution du Big Data et ses mégadonnées, et explore les nouveaux challenges de l’Open Data, qui rend ces informations accessibles, exploitables et réutilisables par tous. Il détaille les facteurs-clés de leur valorisation (Data Mining, Text Mining…) pour donner aux entreprises l’ensemble des outils indispensables pour s’adapter à la nouvelle économie de l’immatériel. Note de contenu : Introduction
1. La révolution du Big Data ou des mégadonnées
2. L’Open Data : un nouveau challenge
3. Les mécanismes de valorisation des données
4. Valorisation et traitement des données
ConclusionNuméro de notice : 22525 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/SOCIETE NUMERIQUE Nature : Monographie Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81511 Réservation
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Code-barres Cote Support Localisation Section Disponibilité 22525-01 18.30 Livre Centre de documentation Sciences de l'information Disponible BIM and cultural heritage: compatibility tests in an archaeological site / Nora Lombardini ; Cinzia Tommasi in International journal of 3-D information modeling, vol 5 n° 1 (January - March 2016)
[article]
Titre : BIM and cultural heritage: compatibility tests in an archaeological site Type de document : Article/Communication Auteurs : Nora Lombardini, Auteur ; Cinzia Tommasi, Auteur Année de publication : 2016 Article en page(s) : pp 29 - 44 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] archéologie
[Termes IGN] base de données localisées
[Termes IGN] base de données thématiques
[Termes IGN] exploration de données
[Termes IGN] logiciel de dessin
[Termes IGN] modélisation 3D du bâti BIM
[Termes IGN] surveillance d'ouvrageRésumé : (Auteur) Nowadays, when people talk about BIM software, they refer to new constructions, made of regular elements and standard parameters. The question is: what happens when one, instead of a new building, considers an ancient or existing one? To answer this question, the possibility of using effectively the BIM process for the maintenance and the conservation of Cultural Heritage has to be evaluated. In particular, the research examines the case study of the archaeological site of the Sanctuary of Diana in Nemi (Rome), which is an important example in the National Heritage. The final product consists of a parametric model, where one can find, embedded in a database, all the information about the elements for the management of conservation projects. Highlighting positive aspects and existing limits, in order to contribute to further studies on the subject, leading to a mandatory implementation and use of this software, even in relation to future European legislative framework. Numéro de notice : A2016--039 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.4018/IJ3DIM.2016010103 En ligne : http://doi.org/10.4018/IJ3DIM.2016010103 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83962
in International journal of 3-D information modeling > vol 5 n° 1 (January - March 2016) . - pp 29 - 44[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 138-2016011 RAB Revue Centre de documentation En réserve L003 Disponible
Titre : Deep learning Type de document : Guide/Manuel Auteurs : Ian Goodfellow, Auteur ; Yoshua Bengio, Auteur ; Aaron Courville, Auteur Editeur : Cambridge [Massachusetts - Etats-Unis] : MIT Press Année de publication : 2016 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] algèbre linéaire
[Termes IGN] apprentissage profond
[Termes IGN] inférence
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] réseau neuronal artificiel
[Termes IGN] réseau neuronal convolutifRésumé : (auteur) The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. The online version of the book is now complete and will remain available online for free. Numéro de notice : 17527 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Manuel de cours En ligne : http://www.deeplearningbook.org Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90631 Dense image matching / Martin Kodde in GIM international [en ligne], vol 30 n° 1 (January 2016)
[article]
Titre : Dense image matching Type de document : Article/Communication Auteurs : Martin Kodde, Auteur Année de publication : 2016 Article en page(s) : pp 28 -29 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] accentuation d'image
[Termes IGN] données massives
[Termes IGN] semis de points
[Termes IGN] vision par ordinateurRésumé : (éditeur) Point clouds are increasingly a prime data source for 3D information. For many years, Lidar systems have been the primary way to create point clouds. More recently, advances in the field of computer vision have allowed for the generation of detailed and reliable point clouds from images - not only from traditional aerial photographs but also from uncalibrated photos from consumer-grade cameras. Dense image matching is the powerful technology underpinning this development. Numéro de notice : A2016-015 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79347
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