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From RTK to PPP-RTK: towards real-time kinematic precise point positioning to support autonomous driving of inland waterway vessels / Xiangdong An in GPS solutions, vol 27 n° 2 (April 2023)  

Public [article]  inGPS solutions > vol 27 n° 2 (April 2023) . - n° 86 Titre : From RTK to PPP-RTK: towards real-time kinematic precise point positioning to support autonomous driving of inland waterway vessels Type de document : Article/Communication Auteurs : Xiangdong An, Auteur ; Ralf Ziebold, Auteur ; Christoph Laas, Auteur Année de publication : 2023 Article en page(s) : n° 86 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement [Termes IGN] filtre de Kalman [Termes IGN] navigation autonome [Termes IGN] navigation fluviale [Termes IGN] positionnement cinématique en temps réel [Termes IGN] positionnement ponctuel précis [Termes IGN] résolution d'ambiguïté [Termes IGN] station GNSS [Termes IGN] temps de convergence Résumé : (auteur) PPP-RTK is Precise Point Positioning (PPP) using corrections from a ground reference network, which enables single-receiver users with integer ambiguity resolution thereby improving its performance. However, most of the PPP-RTK studies are investigated and evaluated in a static situation or a post-processing mode because of the complexity of implementation in real-time practical applications. Moreover, although PPP-RTK achieves a faster convergence than PPP, it typically needs 30 s or even longer to derive high-accuracy results. We have implemented a real-time PPP-RTK approach based on undifferenced observations and State-Space Representation corrections with a fast convergence of less than 30 s to support autonomous driving of inland waterway vessels. The PPP-RTK performances and their feasibility to support autonomous driving have been evaluated and validated in a real-time inland waterway navigation. It proves the PPP-RTK approach can realize a precise positioning of less than 10 cm in horizontal with a rapid convergence. The convergence time is within 10 s after a normal bridge passing and less than 30 s after a complicated bridge passing. Moreover, the PPP-RTK approach can be extended to outside of the GNSS station network. Even if the location is 100 km away from the border of the GNSS station network, the PPP-RTK convergence time after a bridge passing is also normally less than 30 s. We have realized the first automated entry into a waterway lock for a vessel supported by PPP-RTK and taken the first step toward autonomous driving of inland vessels based on PPP-RTK. Numéro de notice : A2023-156 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1007/s10291-023-01428-2 En ligne : https://doi.org/10.1007/s10291-023-01428-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102843 [article]

A unified cycle-slip, multipath estimation, detection and mitigation method for VIO-aided PPP in urban environments / Bo Xu in GPS solutions, vol 27 n° 2 (April 2023)  

Public [article]  inGPS solutions > vol 27 n° 2 (April 2023) . - n° 59 Titre : A unified cycle-slip, multipath estimation, detection and mitigation method for VIO-aided PPP in urban environments Type de document : Article/Communication Auteurs : Bo Xu, Auteur ; Shoujian Zhang, Auteur ; Kaifa Kuang, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 59 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes IGN] filtre de Kalman [Termes IGN] glissement de cycle [Termes IGN] milieu urbain [Termes IGN] modèle stochastique [Termes IGN] navigation autonome [Termes IGN] navigation inertielle [Termes IGN] odomètre [Termes IGN] phase [Termes IGN] positionnement ponctuel précis [Termes IGN] signal GNSS [Termes IGN] trajet multiple Résumé : (auteur) Accurate, continuous and reliable positioning is required in autonomous driving. The precise point positioning (PPP) technique, which can provide a global accurate positioning service using a single global navigation satellite system (GNSS) receiver, has attracted much attention. Nevertheless, due to the cycle slips and multipath effects in the GNSS signal, the performance of PPP is severely degraded in urban areas, which has a negative effect on the PPP/inertial navigation system (INS)/vision integrated navigation. Moreover, the carrier phase observations with un-modeled multipath cause false detection of small cycle slips and lead to deviation in the state variable estimation in PPP. Therefore, an effective cycle slip/multipath estimation, detection and mitigation (EDM) method is proposed. A clustering method is used to separate the cycle slips and multipath from the carrier phase observations aided by visual inertial odometry (VIO) positioning results. The influence of the carrier phase multipath on state variable estimation is reduced by adjusting the stochastic ambiguity model in the Kalman filter. The proposed EDM method is validated by vehicle experiments conducted in urban and freeway areas. Experimental results demonstrate that 0.2% cycle slip detection error is achieved by our method. Besides, the multipath estimation accuracy of EDM improves by more than 50% compared with the geometry-based (GB) method. Regarding positioning accuracy, the EDM method has a maximum of 72.2% and 63.2% improvement compared to traditional geometry-free (GF) and GB methods. Numéro de notice : A2023-124 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1007/s10291-023-01396-7 Date de publication en ligne : 17/01/2023 En ligne : https://doi.org/10.1007/s10291-023-01396-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102501 [article]

Spatial-temporal attentive LSTM for vehicle-trajectory prediction / Rui Jiang in ISPRS International journal of geo-information, vol 11 n° 7 (July 2022)  

Public [article]  inISPRS International journal of geo-information > vol 11 n° 7 (July 2022) . - n° 354 Titre : Spatial-temporal attentive LSTM for vehicle-trajectory prediction Type de document : Article/Communication Auteurs : Rui Jiang, Auteur ; Hongyun Xu, Auteur ; Gelian Gong, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 354 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] attention (apprentissage automatique) [Termes IGN] données spatiotemporelles [Termes IGN] navigation autonome [Termes IGN] relation spatiale [Termes IGN] système de transport intelligent [Termes IGN] trajectoire (véhicule non spatial) [Termes IGN] vision par ordinateur Résumé : (auteur) Vehicle-trajectory prediction is essential for intelligent traffic systems (ITS), as it can help autonomous vehicles to plan a safe and efficient path. However, it is still a challenging task because existing studies have mainly focused on the spatial interactions of adjacent vehicles regardless of the temporal dependencies. In this paper, we propose a spatial-temporal attentive LSTM encoder–decoder model (STAM-LSTM) to predict vehicle trajectories. Specifically, the spatial attention mechanism is used to capture the spatial relationships among neighboring vehicles and then obtain the global spatial feature. Meanwhile, the temporal attention mechanism is designed to distinguish the effects of different historical time steps on future trajectory prediction. In addition, the motion feature of vehicles is extracted to reveal the influence of dynamic information on vehicle-trajectory prediction, and is combined with the local and global spatial features to represent the integrated features of the target vehicle at each historical moment. The experiments were conducted on public highway trajectory datasets—US-101 and I-80 in NGSIM—and the results demonstrate that our model achieves state-of-the-art prediction performance. Numéro de notice : A2022-549 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11070354 Date de publication en ligne : 21/06/2022 En ligne : https://doi.org/10.3390/ijgi11070354 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101150 [article]

Summarizing large scale 3D mesh for urban navigation / Imeen Ben Salah in Robotics and autonomous systems, vol 152 (June 2022)  

Public [article]  inRobotics and autonomous systems > vol 152 (June 2022) . - n° 104037 Titre : Summarizing large scale 3D mesh for urban navigation Type de document : Article/Communication Auteurs : Imeen Ben Salah, Auteur ; Sébastien Kramm, Auteur ; Cédric Demonceaux, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 104037 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme ICP [Termes IGN] carte en 3D [Termes IGN] données lidar [Termes IGN] entropie [Termes IGN] image hémisphérique [Termes IGN] image RVB [Termes IGN] information sémantique [Termes IGN] localisation basée vision [Termes IGN] maillage [Termes IGN] navigation autonome [Termes IGN] précision géométrique (imagerie) [Termes IGN] précision radiométrique [Termes IGN] profondeur [Termes IGN] Rouen [Termes IGN] saillance [Termes IGN] zone urbaine Résumé : (auteur) Cameras have become increasingly common in vehicles, smartphones, and advanced driver assistance systems. The areas of application of these cameras in the world of intelligent transportation systems are becoming more and more varied: pedestrian detection, line crossing detection, navigation, …A major area of research currently focuses on mapping that is essential for localization and navigation. However, this step generates an important problem of memory management. Indeed, the memory space required to accommodate the map of a small city is measured in tens gigabytes. In addition, several providers today are competing to produce High-Definition (HD) maps. These maps offer a rich and detailed representation of the environment for highly accurate localization. However, they require a large storage capacity and high transmission and update costs. To overcome these problems, we propose a solution to summarize this type of map by reducing the size while maintaining the relevance of the data for navigation based on vision only. The summary consists in a set of spherical images augmented by depth and semantic information and allowing to keep the same level of visibility in every directions. These spheres are used as landmarks to offer guidance information to a distant agent. They then have to guarantee, at a lower cost, a good level of precision and speed during navigation. Some experiments on real data demonstrate the feasibility for obtaining a summarized map while maintaining a localization with interesting performances. Numéro de notice : A2022-290 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.robot.2022.104037 Date de publication en ligne : 03/02/2022 En ligne : https://doi.org/10.1016/j.robot.2022.104037 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100335 [article]

Analysis of pedestrian movements and gestures using an on-board camera to predict their intentions / Joseph Gesnouin (2022)  

Public 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 automatique Langues : 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 ordinateur Index. 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- Conclusion Numéro 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://tel.hal.science/tel-03813520 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102091

Domain adaptation for urban scene segmentation / Antoine Saporta (2022)  

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Reshaping perception for autonomous driving with semantic keypoints / Lorenzo Bertoni (2022)  

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Robust approach for urban road surface extraction using mobile laser scanning 3D point clouds / Abdul Nurunnabi (2022)  

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Unsupervised generative models for data analysis and explainable artificial intelligence / Mohanad Abukmeil (2022)  

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A world model enabling information integrity for autonomous vehicles / Corentin Sanchez (2022)  

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Three-dimensional reconstruction of single input image based on point cloud / Yu Hou in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 7 (July 2021)  

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Deep traffic light detection by overlaying synthetic context on arbitrary natural images / Jean Pablo Vieira de Mello in Computers and graphics, vol 94 n° 1 (February 2021)  

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Improving trajectory estimation using 3D city models and kinematic point clouds / Lucas Lucks in Transactions in GIS, Vol 25 n° 1 (February 2021)  

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3D object detection using lidar point clouds and 2D image object detection / Topi Miekkala (2021)  

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3D urban scene understanding by analysis of LiDAR, color and hyperspectral data / David Duque-Arias (2021)  

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