Descripteur
Documents disponibles dans cette catégorie (105)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
Titre : A world model enabling information integrity for autonomous vehicles Type de document : Thèse/HDR Auteurs : Corentin Sanchez, Auteur ; Philippe Bonnifait, Directeur de thèse ; Philippe Xu, Directeur de thèse Editeur : Compiègne : Université de Technologie de Compiègne UTC Année de publication : 2022 Importance : 198 p. Format : 21 x 30 cm Note générale : Bibliographie
Thèse de Doctorat de l'Université de Technologie de Compiègne, Spécialité Automatique et RobotiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] carte routière
[Termes IGN] données multisources
[Termes IGN] information sémantique
[Termes IGN] intégrité des données
[Termes IGN] milieu urbain
[Termes IGN] navigation autonome
[Termes IGN] raisonnement
[Termes IGN] réseau routier
[Termes IGN] robot mobile
[Termes IGN] sécurité routière
[Termes IGN] véhicule sans pilote
[Termes IGN] vision par ordinateurIndex. décimale : THESE Thèses et HDR Résumé : (auteur) To drive in complex urban environments, autonomous vehicles need to understand their driving context. This task, also known as the situation awareness, relies on an internal virtual representation of the world made by the vehicle, called world model. This representation is generally built from information provided by multiple sources. High definition navigation maps supply prior information such as road network topology, geometric description of the carriageway, and semantic information including traffic laws. The perception system provides a description of the space and of road users evolving in the vehicle surroundings. Conjointly, they provide representations of the environment (static and dynamic) and allow to model interactions. In complex situations, a reliable and non-misleading world model is mandatory to avoid inappropriate decision-making and to ensure safety. The goal of this PhD thesis is to propose a novel formalism on the concept of world model that fulfills the situation awareness requirements for an autonomous vehicle. This world model integrates prior knowledge on the road network topology, a lane-level grid representation, its prediction over time and more importantly a mechanism to control and monitor the integrity of information. The concept of world model is present in many autonomous vehicle architectures but may take many various forms and sometimes only implicitly. In some work, it is part of the perception process when in some other it is part of a decisionmaking process. The first contribution of this thesis is a survey on the concept of world model for autonomous driving covering different levels of abstraction for information representation and reasoning. Then, a novel representation is proposed for the world model at the tactical level combining dynamic objects and spatial occupancy information. First, a graph based top-down approach using a high-definition map is proposed to extract the areas of interests with respect to the situation from the vehicle's perspective. It is then used to build a Lane Grid Map (LGM), which is an intermediate space state representation from the ego-vehicle point of view. A top-down approach is chosen to assess and characterize the relevant information of the situation. Additionally to classical free-occupied states, the unknown state is further characterized by the notions of neutralized and safe areas that provide a deeper level of understanding of the situation. Another contribution to the world model is an integrity management mechanism that is built upon the LGM representation. It consists in managing the spatial sampling of the grid cells in order to take into account localization and perception errors and to avoid misleading information. Regardless of the confidence on localization and perception information, the LGM is capable of providing reliable information to decision making in order not to take hazardous decisions.The last part of the situation awareness strategy is the prediction of the world model based on the LGM representation. The main contribution is to show how a classical object-level prediction fits this representation and that the integrity can also be extended at the prediction stage. It is also depicted how a neutralized area can be used in the prediction stage to provide a better situation prediction. The work relies on experimental data in order to demonstrate a real application of a complex situation awareness representation. The approach is evaluated with real data obtained thanks to several experimental vehicles equipped with LiDAR sensors and IMU with RTK corrections in the city of Compi_egne. A high-definition map has also been used in the framework of the SIVALab joint laboratory between Renault and Heudiasyc CNRS-UTC. The world model module has been implemented (with ROS software) in order to fulfll real-time application and is functional on the experimental vehicles for live demonstrations. Note de contenu : General introduction
1- World model for autonomous vehicules
2- An architecture for WM
3- A lane level world model
4- Set-based LGM prediction
General conclusionNuméro de notice : 24089 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Automatique et Robotique : UTC Compiègne : 2022 Organisme de stage : Laboratoire Heudiasyc DOI : sans En ligne : https://www.theses.fr/2022COMP2683 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102509 VGI3D: an interactive and low-cost solution for 3D building modelling from street-level VGI images / Chaoquan Zhang in Journal of Geovisualization and Spatial Analysis, vol 5 n° 2 (December 2021)
[article]
Titre : VGI3D: an interactive and low-cost solution for 3D building modelling from street-level VGI images Type de document : Article/Communication Auteurs : Chaoquan Zhang, Auteur ; Hongchao Fan, Auteur ; Gefei Kong, Auteur Année de publication : 2021 Article en page(s) : n° 18 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] analyse de sensibilité
[Termes IGN] approche participative
[Termes IGN] base de données relationnelles
[Termes IGN] CityGML
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données localisées des bénévoles
[Termes IGN] information sémantique
[Termes IGN] interactivité
[Termes IGN] modélisation 3D du bâti BIM
[Termes IGN] reconstruction 3D du bâtiRésumé : (auteur) Applications in smart cities are inseparable from the usage of three-dimensional (3D) building models. However, the cost of generating and constructing 3D building models with semantic information is high both in time and in labour. To solve this problem, we developed a web-based interactive system, VGI3D, with the ambition of becoming a VGI platform to collect 3D building models with semantic information by using the power of crowdsourcing. VGI3D is a platform-independent software program that is composed of a spatially relational database (PostgreSQL/PostGIS) for the storage and management of spatially geometrical data and other software modules, allowing users to import, analyse, reconstruct, visualise, modify and export 3D building models according to the OBJ/CityGML standard. In this paper, we present the VGI3D in detail, focusing on relevant technical implementations, and report the results of limited usability testing aimed at optimising the system and user experience. After limited expert and non-expert participants’ testing, we proved the usefulness of VGI3D and its promising value for the 3D modelling community. Numéro de notice : A2021-884 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s41651-021-00086-7 Date de publication en ligne : 23/09/2021 En ligne : https://doi.org/10.1007/s41651-021-00086-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99205
in Journal of Geovisualization and Spatial Analysis > vol 5 n° 2 (December 2021) . - n° 18[article]Utilisation de l'apprentissage profond dans la modélisation 3D urbaine [Partie 1] / Hamza Ben Addou in Géomatique expert, n° 135 (septembre 2021)
[article]
Titre : Utilisation de l'apprentissage profond dans la modélisation 3D urbaine [Partie 1] Type de document : Article/Communication Auteurs : Hamza Ben Addou, Auteur Année de publication : 2021 Article en page(s) : pp 11 - 20 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection du bâti
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] emprise au sol
[Termes IGN] fusion de données multisource
[Termes IGN] image aérienne
[Termes IGN] information sémantique
[Termes IGN] modèle 3D de l'espace urbain
[Termes IGN] segmentation d'image
[Termes IGN] semis de pointsRésumé : (Auteur) Partie 1 : Mise en place d’un processus de détection automatique des emprises de bâtiments par apprentissage profond Numéro de notice : A2021-660 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE/URBANISME Nature : Article nature-HAL : ArtSansCL DOI : sans Date de publication en ligne : 01/09/2021 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98414
in Géomatique expert > n° 135 (septembre 2021) . - pp 11 - 20[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité IFN-001-P002273 PER Revue Nogent-sur-Vernisson Salle périodiques Exclu du prêt Semantic-aware label placement for augmented reality in street view / Jianqing Jia in The Visual Computer, vol 37 n° 7 (July 2021)
[article]
Titre : Semantic-aware label placement for augmented reality in street view Type de document : Article/Communication Auteurs : Jianqing Jia, Auteur ; Semir Elezovikj, Auteur ; Heng Fan, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1805 - 1819 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] image Streetview
[Termes IGN] information sémantique
[Termes IGN] optimisation (mathématiques)
[Termes IGN] point d'intérêt
[Termes IGN] réalité augmentée
[Termes IGN] saillance
[Termes IGN] scène urbaine
[Termes IGN] segmentation sémantiqueRésumé : (auteur) In an augmented reality (AR) application, placing labels in a manner that is clear and readable without occluding the critical information from the real world can be a challenging problem. This paper introduces a label placement technique for AR used in street view scenarios. We propose a semantic-aware task-specific label placement method by identifying potentially important image regions through a novel feature map, which we refer to as guidance map. Given an input image, its saliency information, semantic information and the task-specific importance prior are integrated in the guidance map for our labeling task. To learn the task prior, we created a label placement dataset with the users’ labeling preferences, as well as use it for evaluation. Our solution encodes the constraints for placing labels in an optimization problem to obtain the final label layout, and the labels will be placed in appropriate positions to reduce the chances of overlaying important real-world objects in street view AR scenarios. The experimental validation shows clearly the benefits of our method over previous solutions in the AR street view navigation and similar applications. Numéro de notice : A2021-542 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00371-020-01939-w Date de publication en ligne : 02/08/2020 En ligne : https://doi.org/10.1007/s00371-020-01939-w Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98022
in The Visual Computer > vol 37 n° 7 (July 2021) . - pp 1805 - 1819[article]Spatial knowledge acquisition with virtual semantic landmarks in mixed reality-based indoor navigation / Bing Liu in Cartography and Geographic Information Science, vol 48 n° 4 (July 2021)
[article]
Titre : Spatial knowledge acquisition with virtual semantic landmarks in mixed reality-based indoor navigation Type de document : Article/Communication Auteurs : Bing Liu, Auteur ; Linfang Ding, Auteur ; Liqiu Meng, Auteur Année de publication : 2021 Article en page(s) : pp 305 - 319 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] conception orientée utilisateur
[Termes IGN] GPS assisté pour la navigation (technologies)
[Termes IGN] hologramme
[Termes IGN] information sémantique
[Termes IGN] navigation virtuelle
[Termes IGN] point de repère
[Termes IGN] positionnement en intérieur
[Termes IGN] questionnaire
[Termes IGN] réalité mixte
[Termes IGN] téléphone intelligent
[Termes IGN] utilisateur civilRésumé : (auteur) Landmarks are essential and widely used in human navigation. However, many indoor environments lack visually salient landmarks, which leads to difficulties in navigating in and learning complex and similar-looking indoor environments. In this study, we designed and implemented virtual semantic landmarks in Mixed Reality (MR)-based indoor environments and conducted a user study to explore whether such landmarks can assist spatial knowledge acquisition during navigation. More specifically, we employed the untethered, head-mounted mixed reality device Microsoft HoloLens and used iconic holograms to show the semantic landmarks. In the user study, we used sketch map, landmark locating tasks and interview to assess the results of the spatial knowledge acquisition and collect advice on improving the MR-based navigation interface. The results show that virtual semantic landmarks can assist the acquisition of corresponding knowledge, as such landmarks were labeled second most often in landmark locating task. In addition, individual cases show that head-mounted mixed reality devices may influence not only vision, but also height or time perception of certain users. Our result can be applied to facilitate the design of MR-based navigation interfaces and assist spatial knowledge acquisition. Numéro de notice : A2021-445 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2021.1908171 Date de publication en ligne : 22/04/2021 En ligne : https://doi.org/10.1080/15230406.2021.1908171 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97852
in Cartography and Geographic Information Science > vol 48 n° 4 (July 2021) . - pp 305 - 319[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 032-2021041 RAB Revue Centre de documentation En réserve L003 Disponible Semantic signatures for large-scale visual localization / Li Weng in Multimedia tools and applications, vol 80 n° 15 (June 2021)PermalinkStop-and-move sequence expressions over semantic trajectories / Yenier Torres Izquierdo in International journal of geographical information science IJGIS, vol 35 n° 4 (April 2021)PermalinkOntology-based semantic conceptualisation of historical built heritage to generate parametric structured models from point clouds / Elisabetta Colucci in Applied sciences, vol 11 n° 6 (March 2021)PermalinkPermalinkLearning to translate land-cover maps: Several multi-dimensional context-wise solutions / Luc Baudoux (2021)PermalinkPermalinkPermalinkSemantic‐based urban growth prediction / Marvin Mc Cutchan in Transactions in GIS, Vol 24 n° 6 (December 2020)PermalinkThe position of sound in audiovisual maps: an experimental study of performance in spatial memory / Nils Siepmann in Cartographica, vol 55 n° 2 (Summer 2020)PermalinkAn IEEE value loop of human-technology collaboration in geospatial information science / Liqiu Meng in Geo-spatial Information Science, vol 23 n° 1 (March 2020)Permalink