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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 ; Shisheng 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 géométrique [Termes IGN] géolocalisation [Termes IGN] image aérienne [Termes IGN] réseau routier [Termes IGN] segmentation d'image [Termes IGN] semis de points [Termes IGN] superposition d'images [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]