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Investigating the role of image retrieval for visual localization / Martin Humenberger in International journal of computer vision, vol 130 n° 7 (July 2022)
Titre : Investigating the role of image retrieval for visual localization Type de document : Article/Communication Auteurs : Martin Humenberger, Auteur ; Yohann Cabon, Auteur ; Noé Pion, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : 1811 - 1836 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse visuelle
[Termes IGN] base de données d'images
[Termes IGN] estimation de pose
[Termes IGN] flou
[Termes IGN] localisation basée image
[Termes IGN] localisation basée vision
[Termes IGN] point de repère
[Termes IGN] précision de localisation
[Termes IGN] Ransac (algorithme)
[Termes IGN] réalité de terrain
[Termes IGN] structure-from-motion
[Termes IGN] vision par ordinateurRésumé : (auteur) Visual localization, i.e., camera pose estimation in a known scene, is a core component of technologies such as autonomous driving and augmented reality. State-of-the-art localization approaches often rely on image retrieval techniques for one of two purposes: (1) provide an approximate pose estimate or (2) determine which parts of the scene are potentially visible in a given query image. It is common practice to use state-of-the-art image retrieval algorithms for both of them. These algorithms are often trained for the goal of retrieving the same landmark under a large range of viewpoint changes which often differs from the requirements of visual localization. In order to investigate the consequences for visual localization, this paper focuses on understanding the role of image retrieval for multiple visual localization paradigms. First, we introduce a novel benchmark setup and compare state-of-the-art retrieval representations on multiple datasets using localization performance as metric. Second, we investigate several definitions of “ground truth” for image retrieval. Using these definitions as upper bounds for the visual localization paradigms, we show that there is still significant room for improvement. Third, using these tools and in-depth analysis, we show that retrieval performance on classical landmark retrieval or place recognition tasks correlates only for some but not all paradigms to localization performance. Finally, we analyze the effects of blur and dynamic scenes in the images. We conclude that there is a need for retrieval approaches specifically designed for localization paradigms. Our benchmark and evaluation protocols are available at https://github.com/naver/kapture-localization. Numéro de notice : A2022-538 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s11263-022-01615-7 Date de publication en ligne : 25/05/2022 En ligne : https://doi.org/10.1007/s11263-022-01615-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101070
in International journal of computer vision > vol 130 n° 7 (July 2022) . - 1811 - 1836[article]Analysis of pedestrian movements and gestures using an on-board camera to predict their intentions / Joseph Gesnouin (2022)
Titre : Analysis of pedestrian movements and gestures using an on-board camera to predict their intentions Titre original : Analyse des mouvements et gestes des piétons via caméra embarquée pour la prédiction de leurs intentions Type de document : Thèse/HDR Auteurs : Joseph Gesnouin, Auteur ; Fabien Moutarde, Directeur de thèse Editeur : Paris : Université Paris Sciences et Lettres Année de publication : 2022 Importance : 171 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de doctorat de l'Université Paris Sciences et Lettres, Préparée à MINES ParisTech, Spécialité
Informatique temps réel, robotique et automatiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] apprentissage profond
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par réseau neuronal récurrent
[Termes IGN] estimation de pose
[Termes IGN] image RVB
[Termes IGN] instrument embarqué
[Termes IGN] navigation autonome
[Termes IGN] piéton
[Termes IGN] reconnaissance de gestes
[Termes IGN] réseau neuronal de graphes
[Termes IGN] squelettisation
[Termes IGN] trajectoire (véhicule non spatial)
[Termes IGN] vision par ordinateurIndex. décimale : THESE Thèses et HDR Résumé : (auteur) The autonomous vehicle (AV) is a major challenge for the mobility of tomorrow. Progress is being made every day to achieve it; however, many problems remain to be solved to achieve a safe outcome for the most vulnerable road users (VRUs). One of the major challenge faced by AVs is the ability to efficiently drive in urban environments. Such a task requires interactions between autonomous vehicles and VRUs to resolve traffic ambiguities. In order to interact with VRUs, AVs must be able to understand their intentions and predict their incoming actions. In this dissertation, our work revolves around machine learning technology as a way to understand and predict human behaviour from visual signals and more specifically pose kinematics. Our goal is to propose an assistance system to the AV that is lightweight, scene-agnostic that could be easily implemented in any embedded devices with real-time constraints. Firstly, in the gesture and action recognition domain, we study and introduce different representations for pose kinematics, based on deep learning models as a way to efficiently leverage their spatial and temporal components while staying in an euclidean grid-space. Secondly, in the autonomous driving domain, we show that it is possible to link the posture, the walking attitude and the future behaviours of the protagonists of a scene without using the contextual information of the scene (zebra crossing, traffic light...). This allowed us to divide by a factor of 20 the inference speed of existing approaches for pedestrian intention prediction while keeping the same prediction robustness. Finally, we assess the generalization capabilities of pedestrian crossing predictors and show that the classical train-test sets evaluation for pedestrian crossing prediction, i.e., models being trained and tested on the same dataset, is not sufficient to efficiently compare nor conclude anything about their applicability in a real-world scenario. To make the research field more sustainable and representative of the real advances to come. We propose new protocols and metrics based on uncertainty estimates under domain-shift in order to reach the end-goal of pedestrian crossing behavior predictors: vehicle implementation. Note de contenu : 1- Introduction
2- Human activity recognition with pose-driven deep learning models
3- From action recognition to pedestrian discrete intention prediction
4- Assessing the generalization of pedestrian crossing predictors
5- ConclusionNuméro de notice : 24066 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Informatique temps réel, robotique et automatique : Paris Sciences et Lettres : 2022 DOI : sans En ligne : https://pastel.archives-ouvertes.fr/tel-03813520 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102091
Titre : Automatic algorithm for georeferencing historical-to-nowadays aerial images acquired in natural environments Type de document : Article/Communication Auteurs : Daniela Craciun Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2022 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 43-B2 Projets : HIATUS / Giordano, Sébastien Conférence : ISPRS 2022, XXIV ISPRS international congress, Imaging today, foreseeing tomorrow 06/06/2022 11/06/2022 Nice France OA ISPRS Archives Importance : pp 21 - 28 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique
[Termes IGN] appariement d'images
[Termes IGN] estimation de pose
[Termes IGN] géoréférencement
[Termes IGN] gradient
[Termes IGN] histogramme
[Termes IGN] image ancienne
[Termes IGN] milieu naturel
[Termes IGN] modèle numérique de surfaceRésumé : (auteur) Automatic georeferencing for historical-to-nowadays aerial images represents the main ingredient for supplying territory evolution analysis and environmental monitoring. Existing georeferencing methods based on feature extraction and matching reported successful results for multi-epoch aerial images acquired in structured and man-made environments. While improving the state-of-the-art of the multi-epoch georeferencing problem, such frameworks present several limitations when applied to unstructured scenes, such as natural feature-less environments, characterized by homogenous or texture-less areas. This is mainly due to the lack of structured areas which often results in sparse and ambiguous feature matches, introducing inconsistencies during the pose estimation process. This paper addresses the automatic georeferencing problem for historical aerial images acquired in unstructured natural environments. The research work presented in this paper introduces a feature-less algorithm designed to perform historical-to-nowadays image matching for pose estimation in a fully automatic fashion. The proposed algorithm operates within two stages: (i) 2D patch extraction and matching and (ii) 3D patch-based local alignment. The final output is a set of 3D patch matches and the 3D rigid transformation relating each homologous patches. The obtained 3D point matches are designed to be injected into traditional multi-views pose optimisation engines. Experimental results on real datasets acquired over Fabas area situated in France demonstrate the effectiveness of the proposed method. Our findings illustrate that the proposed georeferencing technique provides accurate results in presence of large periods of time separating historical from nowadays aerial images (up to 48 years time span). Numéro de notice : C2022-020 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprs-archives-XLIII-B2-2022-21-2022 Date de publication en ligne : 30/05/2022 En ligne : http://dx.doi.org/10.5194/isprs-archives-XLIII-B2-2022-21-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100846
Titre : Robust GNSS carrier phase-based position and attitude estimation theory and applications Type de document : Thèse/HDR Auteurs : Daniel Arias Medina, Auteur Editeur : Madrid [Espagne] : Universidad Carlos III Année de publication : 2022 Importance : 249 p. Format : 21 x 30 cm Note générale : bibliographie
A dissertation submitted by in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science and Technology, Universidad Carlos III de MadridLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] classification du maximum a posteriori
[Termes IGN] constellation GNSS
[Termes IGN] estimation de pose
[Termes IGN] filtrage du bruit
[Termes IGN] méthode du maximum de vraisemblance (estimation)
[Termes IGN] phase GNSS
[Termes IGN] positionnement cinématique en temps réel
[Termes IGN] résolution d'ambiguïté
[Termes IGN] signal GNSSIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Navigation information is an essential element for the functioning of robotic platforms and intelligent transportation systems. Among the existing technologies, Global Navigation Satellite Systems (GNSS) have established as the cornerstone for outdoor navigation, allowing for all-weather, all-time positioning and timing at a worldwide scale. GNSS is the generic term for referring to a constellation of satellites which transmit radio signals used primarily for ranging information. Therefore, the successful operation and deployment of prospective autonomous systems is subject to our capabilities to support GNSS in the provision of robust and precise navigational estimates. GNSS signals enable two types of ranging observations: –code pseudorange, which is a measure of the time difference between the signal’s emission and reception at the satellite and receiver, respectively, scaled by the speed of light; –carrier phase pseudorange, which measures the beat of the carrier signal and the number of accumulated full carrier cycles. While code pseudoranges provides an unambiguous measure of the distance between satellites and receiver, with a dm-level precision when disregarding atmospheric delays and clock offsets, carrier phase measurements present a much higher precision, at the cost of being ambiguous by an unknown number of integer cycles, commonly denoted as ambiguities. Thus, the maximum potential of GNSS, in terms of navigational precision, can be reach by the use of carrier phase observations which, in turn, lead to complicated estimation problems. This thesis deals with the estimation theory behind the provision of carrier phase-based precise navigation for vehicles traversing scenarios with harsh signal propagation conditions. Contributions to such a broad topic are made in three directions. First, the ultimate positioning performance is addressed, by proposing lower bounds on the signal processing realized at the receiver level and for the mixed real- and integer-valued problem related to carrier phase-based positioning. Second, multi-antenna configurations are considered for the computation of a vehicle’s orientation, introducing a new model for the joint position and attitude estimation problems and proposing new deterministic and recursive estimators based on Lie Theory. Finally, the framework of robust statistics is explored to propose new solutions to code- and carrier phase-based navigation, able to deal with outlying impulsive noises. Note de contenu : Introduction
I- A signal processing approach to satellite-based navigation
II- On the position and attitude estimation in multi-antenna GNSS
III- Robust estimation for navigation in harsh environments
Conclusions and future researchNuméro de notice : 15279 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Thèse étrangère Note de thèse : PhD Thesis : Computer Science and Technology : Carlos III Madrid : 2022 Organisme de stage : German Aerospace Center DOI : sans En ligne : https://e-archivo.uc3m.es/handle/10016/35375#preview Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101116
Titre : Robustness of visual SLAM techniques to light changing conditions : Influence of contrasted local features, multi-planar representations and multimodal image analysis Type de document : Thèse/HDR Auteurs : Xi Wang, Auteur ; Eric Marchand, Directeur de thèse Editeur : Rennes : Université de Rennes 1 Année de publication : 2022 Importance : 153 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de Doctorat de l'Université de Rennes 1, Spécialité InformatiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] cartographie et localisation simultanées
[Termes IGN] éclairage
[Termes IGN] estimation de pose
[Termes IGN] information sémantique
[Termes IGN] primitive géométrique
[Termes IGN] programmation linéaire
[Termes IGN] robotique
[Termes IGN] vision par ordinateurIndex. décimale : THESE Thèses et HDR Résumé : (auteur) The SLAM (Simultaneous Localization And Mapping) technique concentrates on localizing and recovering the environment in a simultaneous way and is one of the core functionalities of many industrial products such as augmented reality, where the device poses should be tracked in real-time; autonomous driving, where one needs to localize the vehicle in a pre-generated map or unknown environment; and even modern filmmaking workflow, where the relative camera position and orientation are critical for post-processing or real-time prevising for directors and actors to visualise the visual effects on the stage. Multiple difficulties in different levels can influence the final performance of robot agents’s SLAM task, as the pipeline is long and complicated from the real world physics to the required information such as agent poses and 3-D map, which help us visualize colourful graphics scenes in AR devices or make hard decisions on the highway for autonomous driving. Many solutions are proposed for addressing each problem, respectively, with the means from classic statistic probability models to the modern data-driven deep neural network. However, the quest of improving the robot’s robustness under dynamic and complicated environments perisists and becomes more and more significant and active for nowadays robotics research. The need for improving the robustness of robot agents is imminent and regarded as one of most imperative factors for deploying robots ubiquitously in our daily life. Under this context, this thesis tries to address a small drop in the ocean of the problem of SLAM robustness, yet in a very systematic view: we try to break down the SLAM system into different and inter-influential modules. Then use the concept of "divide and conquer" for answering possible questions within each module and wishing to contribute to the community and help improve the robustness of SLAM systems under complicated conditions. With the above objectives, the contributions of the thesis are stated as follows for tackling the robustness problem from multiple angles: 1) From the image feature angle, we proposed a multiple layered image structure for improving the performance of traditional local image features under extreme conditions. Furthermore, an optimization method on linear searching and mutual information assisted convex optimization are designed for tuning the optimal parameters with the proposed structure; 2) From the geometric primitive angle, we proposed a relative pose estimation and SLAM framework under the multiple planar assumption, by keypoint feature-based and template tracker based methods, respectively. We tried to achieve better performance of mapping and tracking simultaneously with the help of a more general planar assumption. 3) From the angle of relocalization of the SLAM system, the idea is to recover the already passed locations of the robot agent for lowering the overall estimation error or when the robot is in lost status. We proposed a binary graph structure for embedding spatial information and heterogeneous data formats such as depth image, semantic information etc. The proposed method enables robotics SLAM systems to relocalize themselves with a higher success rate even under different lighting, weather and seasonal conditions. Note de contenu : 1- Introduction
2- Résumé
3- Background on visual SLAM techniques
4- Related work
5- Organisation
6- Multiple layers image
7- Multi-planar relative pose estimation via superpixel
8- TT-SLAM
9- Binary graph descriptor for robust relocalization on heterogeneous data
ConclusionNuméro de notice : 24074 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Informatique : Rennes 1 : 2022 Organisme de stage : IRISA DOI : sans En ligne : https://www.theses.fr/2022REN1S022 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102162
