Centre dedocumentation
scientifique

Road-network-based fast geolocalization / Yongfei Li in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 7 (July 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 59 n° 7 (July 2021) . - pp 6065 - 6076 Titre : Road-network-based fast geolocalization Type de document : Article/Communication Auteurs : Yongfei Li, Auteur ; Dongfang Yang, Auteur ; Shicheng Wang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 6065 - 6076 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] carrefour [Termes IGN] carte routière [Termes IGN] cohérence [Termes IGN] enregistrement de données [Termes IGN] géolocalisation [Termes IGN] image aérienne [Termes IGN] réseau routier [Termes IGN] segmentation d'image [Termes IGN] transformation homographique [Termes IGN] zone urbaine Résumé : (auteur) In this article, a road-network-based geolocalization method is proposed. We match roads in the onboard images to the reference road vector map, and realize successful localization over areas as large as a whole city. The road network matching problem is treated as a point cloud registration problem under the homography transformation and solved under the hypothesize-and-test framework. To tackle the point cloud registration problem, a global projective-invariant feature is proposed, which consists of two road intersections augmented with their tangents. In addition, we propose the necessary conditions for the features to match. This can reduce the candidate matching features, thus accelerating the search to a great extent. These matching candidates are first “filtered” with the model consistency check in parameter space and then tested with similarity metrics to identify the correct transformation. The experiments show that our method can localize an aerial image over an area larger than 1000 km 2 within several seconds on a single CPU. Our code can be found at: https://github.com/FlyAlCode/RCLGeolocalization-2.0 . Numéro de notice : A2021-532 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3011034 Date de publication en ligne : 18/08/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3011034 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97989 [article]

Extraction of street pole-like objects based on plane filtering from mobile LiDAR data / Jingming Tu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 1 (January 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 1 (January 2021) . - pp 749 - 768 Titre : Extraction of street pole-like objects based on plane filtering from mobile LiDAR data Type de document : Article/Communication Auteurs : Jingming Tu, Auteur ; Jian Yao, Auteur ; Li Li, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 749 - 768 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse d'image orientée objet [Termes IGN] carte routière [Termes IGN] détection d'objet [Termes IGN] données lidar [Termes IGN] extraction de traits caractéristiques [Termes IGN] forme caractéristique [Termes IGN] méthode robuste [Termes IGN] octree [Termes IGN] réseau routier Résumé : (auteur) Pole-like objects provide important street infrastructure for road inventory and road mapping. In this article, we proposed a novel pole-like object extraction algorithm based on plane filtering from mobile Light Detection and Ranging (LiDAR) data. The proposed approach is composed of two parts. In the first part, a novel octree-based split scheme was proposed to fit initial planes from off-ground points. The results of the plane fitting contribute to the extraction of pole-like objects. In the second part, we proposed a novel method of pole-like object extraction by plane filtering based on local geometric feature restriction and isolation detection. The proposed approach is a new solution for detecting pole-like objects from mobile LiDAR data. The innovation in this article is that we assumed that each of the pole-like objects can be represented by a plane. Thus, the essence of extracting pole-like objects will be converted to plane selecting problem. The proposed method has been tested on three data sets captured from different scenes. The average completeness, correctness, and quality of our approach can reach up to 87.66%, 88.81%, and 79.03%, which is superior to state-of-the-art approaches. The experimental results indicate that our approach can extract pole-like objects robustly and efficiently. Numéro de notice : A2021-042 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2993454 Date de publication en ligne : 20/05/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2993454 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96758 [article]

Road network simplification for location-based services / Abdeltawab M. Hendawi in Geoinformatica [en ligne], vol 24 n° 4 (October 2020)  

Public [article]  inGeoinformatica [en ligne] > vol 24 n° 4 (October 2020) . - pp 801 - 826 Titre : Road network simplification for location-based services Type de document : Article/Communication Auteurs : Abdeltawab M. Hendawi, Auteur ; John A. Stankovic, Auteur ; Ayman Taha, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 801 - 826 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] algorithme de Douglas-Peucker [Termes IGN] appariement de cartes [Termes IGN] appariement de données localisées [Termes IGN] appariement de graphes [Termes IGN] carte routière [Termes IGN] compression de données [Termes IGN] modèle de Markov caché [Termes IGN] réseau routier [Termes IGN] service fondé sur la position [Termes IGN] simplification de contour [Termes IGN] stockage de données [Vedettes matières IGN] Généralisation Résumé : (Auteur) Road-network data compression or simplification reduces the size of the network to occupy less storage with the aim to fit small form-factor routing devices, mobile devices, or embedded systems. Simplification (a) reduces the storage cost of memory and disks, and (b) reduces the I/O and communication overhead. There are several road network compression techniques proposed in the literature. These techniques are evaluated by their compression ratios. However, none of these techniques takes into consideration the possibility that the generated compressed data can be used directly in Map-matching operation which is an essential component for all location-aware services. Map-matching matches a measured latitude and longitude of an object to an edge in the road network graph. In this paper, we propose a novel simplification technique, named COMA, that (1) significantly reduces the size of a given road network graph, (2) achieves high map-matching quality on the simplified graph, and (3) enables the generated compressed road network graph to be used directly in map-matching and location-based applications without a need to decompress it beforehand. COMA smartly deletes those nodes and edges that will not affect the graph connectivity nor causing much of ambiguity in the map-matching of objects’ location. COMA employs a controllable parameter; termed a conflict factor C, whereby location aware services can trade the compression gain with map-matching accuracy at varying granularity. We show that the time complexity of our COMA algorithm is O(|N|log|N|). Intensive experimental evaluation based on a real implementation and data demonstrates that COMA can achieve about a 75% compression-ratio while preserving high map-matching quality. Road Network, Simplification, Compression, Spatial, Location, Performance, Accuracy, Efficiency, Scalability. Numéro de notice : A2020-495 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10707-020-00406-x Date de publication en ligne : 01/05/2020 En ligne : https://doi.org/10.1007/s10707-020-00406-x Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96115 [article]

Traffic signal detection from in-vehicle GPS speed profiles using functional data analysis and machine learning / Yann Méneroux in International Journal of Data Science and Analytics JDSA, vol 10 n° 1 (June 2020)    

Public [article]  inInternational Journal of Data Science and Analytics JDSA > vol 10 n° 1 (June 2020) . - pp 101 - 119 Titre : Traffic signal detection from in-vehicle GPS speed profiles using functional data analysis and machine learning Type de document : Article/Communication Auteurs : Yann Méneroux , Auteur ; Arnaud Le Guilcher , Auteur ; Guillaume Saint Pierre, Auteur ; Mohammad Ghasemi Hamed, Auteur ; Sébastien Mustière , Auteur ; Olivier Orfila, Auteur Année de publication : 2020 Projets : 1-Pas de projet / Article en page(s) : pp 101 - 119 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] analyse fonctionnelle (mathématiques) [Termes IGN] apprentissage profond [Termes IGN] carte routière [Termes IGN] classification par forêts aléatoires [Termes IGN] contenu généré par les utilisateurs [Termes IGN] détection d'objet [Termes IGN] données routières [Termes IGN] feu de circulation [Termes IGN] inférence [Termes IGN] reconnaissance de formes [Termes IGN] signalisation routière [Termes IGN] trace GPS [Termes IGN] trafic routier [Termes IGN] transformation en ondelettes [Termes IGN] vitesse Résumé : (auteur) The increasing availability of large-scale global positioning system data stemming from in-vehicle-embedded terminal devices enables the design of methods deriving road network cartographic information from drivers’ recorded traces. Some machine learning approaches have been proposed in the past to train automatic road network map inference, and recently this approach has been successfully extended to infer road attributes as well, such as speed limitation or number of lanes. In this paper, we address the problem of detecting traffic signals from a set of vehicle speed profiles, under a classification perspective. Each data instance is a speed versus distance plot depicting over a hundred profiles on a 100-m-long road span. We proposed three different ways of deriving features: The first one relies on the raw speed measurements; the second one uses image recognition techniques; and the third one is based on functional data analysis. We input them into most commonly used classification algorithms, and a comparative analysis demonstrated that a functional description of speed profiles with wavelet transforms seems to outperform the other approaches with most of the tested classifiers. It also highlighted that random forests yield an accurate detection of traffic signals, regardless of the chosen feature extraction method, while keeping a remarkably low confusion rate with stop signs. Numéro de notice : A2020-336 Affiliation des auteurs : LaSTIG COGIT+Ext (2012-2019) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s41060-019-00197-x Date de publication en ligne : 04/10/2019 En ligne : https://doi.org/10.1007/s41060-019-00197-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93755 [article]

Documents numériques

peut être téléchargé Traffic signal detection ... - preprint Adobe Acrobat PDF

Application of machine learning techniques for evidential 3D perception, in the context of autonomous driving / Edouard Capellier (2020)  

Public Titre : Application of machine learning techniques for evidential 3D perception, in the context of autonomous driving Type de document : Thèse/HDR Auteurs : Edouard Capellier, Auteur ; Véronique Berge-Cherfaoui, Directeur de thèse ; Franck Davoine, Directeur de thèse Editeur : Compiègne : Université de Technologie de Compiègne UTC Année de publication : 2020 Importance : 123 p. Format : 21 x 30 cm Note générale : bibliographie Thèse présentée pour l'obtention du grade de Docteur de l'UTC, Robotique et Sciences et Technologies de l'Information et des Systèmes Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage automatique [Termes IGN] apprentissage profond [Termes IGN] carte routière [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection d'objet [Termes IGN] données lidar [Termes IGN] image RVB [Termes IGN] intelligence artificielle [Termes IGN] navigation autonome [Termes IGN] segmentation sémantique [Termes IGN] théorie de Dempster-Shafer [Termes IGN] vision par ordinateur [Termes IGN] visualisation 3D Résumé : (auteur) The perception task is paramount for self-driving vehicles. Being able to extract accurate and significant information from sensor inputs is mandatory, so as to ensure a safe operation. The recent progresses of machine-learning techniques revolutionize the way perception modules, for autonomous driving, are being developed and evaluated, while allowing to vastly overpass previous state-of-the-art results in practically all the perception-related tasks. Therefore, efficient and accurate ways to model the knowledge that is used by a self-driving vehicle is mandatory. Indeed, self-awareness, and appropriate modeling of the doubts, are desirable properties for such system. In this work, we assumed that the evidence theory was an efficient way to finely model the information extracted from deep neural networks. Based on those intuitions, we developed three perception modules that rely on machine learning, and the evidence theory. Those modules were tested on real-life data. First, we proposed an asynchronous evidential occupancy grid mapping algorithm, that fused semantic segmentation results obtained from RGB images, and LIDAR scans. Its asynchronous nature makes it particularly efficient to handle sensor failures. The semantic information is used to define decay rates at the cell level, and handle potentially moving object. Then, we proposed an evidential classifier of LIDAR objects. This system is trained to distinguish between vehicles and vulnerable road users, that are detected via a clustering algorithm. The classifier can be reinterpreted as performing a fusion of simple evidential mass functions. Moreover, a simple statistical filtering scheme can be used to filter outputs of the classifier that are incoherent with regards to the training set, so as to allow the classifier to work in open world, and reject other types of objects. Finally, we investigated the possibility to perform road detection in LIDAR scans, from deep neural networks. We proposed two architectures that are inspired by recent state-of-the-art LIDAR processing systems. A training dataset was acquired and labeled in a semi-automatic fashion from road maps. A set of fused neural networks reaches satisfactory results, which allowed us to use them in an evidential road mapping and object detection algorithm, that manages to run at 10 Hz Note de contenu : 1- Introduction 2- Machine learning for perception in autonomous driving 3- The evidence theory, and its applications in autonomous driving 4- A synchronous evidential grid mapping from RGB images and LIDAR scans 5- Evidential LIDAR object classification 6- Road detection in LIDAR scans 7- Application of RoadSeg:evidential road surface mapping 8- Conclusion Numéro de notice : 25895 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Robotique et Sciences et Technologies de l'Information et des Systèmes : UTC : 2020 Organisme de stage : Laboratoire Heudiasyc nature-HAL : Thèse DOI : sans En ligne : https://hal.archives-ouvertes.fr/tel-02897810 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96013

Constraint based evaluation of generalized images generated by deep learning / Azelle Courtial (2020)  

Permalink

The effects of visual realism, spatial abilities, and competition on performance in map-based route learning in men / Arzu Çöltekin in Cartography and Geographic Information Science, Vol 45 n° 4 (July 2018)  

Permalink

Detection and localization of traffic signals with GPS floating car data and Random Forest / Yann Méneroux (2018)    

Permalink

Parcourir et marquer le temps : premiers éléments pour une étude diachronique appliquée à la cartographie d'itinéraire / Quentin Morcette in Cartes & Géomatique, n° 225 (septembre 2015) 

Permalink

Routes visualization: Automated placement of multiple route symbols along a physical network infrastructure / Jules Teulade-Denantes in Journal of Spatial Information Science (JoSIS), n° 11 (September 2015)    

Permalink

L'atlas de Trudaine / Stéphane Blond (2014)

Permalink

Estimation of transformation parameters between centre-line vector road maps and high resolution satellite images / Lu Luping in Photogrammetric record, vol 28 n° 142 (June - August 2013)  

Permalink

Extracting roads from dense point clouds in large scale urban environment / A. Boyko in ISPRS Journal of photogrammetry and remote sensing, vol 66 n° 6 supplement (December 2011)  

Permalink

Michelin, cent ans de cartographie / Philippe Sablayrolles in Le monde des cartes, n° 208 (juin 2011)

Permalink

Michelin : un siècle de cartographie / Anonyme in Géomatique expert, n° 66 (01/01/2009)

Permalink