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Automatic registration of optical aerial imagery to a LiDAR point cloud for generation of city models / Bernard O. Abayowa in ISPRS Journal of photogrammetry and remote sensing, vol 106 (August 2015)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 106 (August 2015) . - pp 68 - 81 Titre : Automatic registration of optical aerial imagery to a LiDAR point cloud for generation of city models Type de document : Article/Communication Auteurs : Bernard O. Abayowa, Auteur ; Alper Yilmaz, Auteur ; Russell C. Hardie, Auteur Année de publication : 2015 Article en page(s) : pp 68 - 81 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] algorithme ICP [Termes IGN] corrélation croisée normalisée [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] image aérienne [Termes IGN] image optique [Termes IGN] méthode robuste [Termes IGN] milieu urbain [Termes IGN] modèle 3D de l'espace urbain [Termes IGN] modèle numérique de surface [Termes IGN] reconstruction 3D [Termes IGN] scène [Termes IGN] semis de points [Termes IGN] superposition d'images Résumé : (auteur) This paper presents a framework for automatic registration of both the optical and 3D structural information extracted from oblique aerial imagery to a Light Detection and Ranging (LiDAR) point cloud without prior knowledge of an initial alignment. The framework employs a coarse to fine strategy in the estimation of the registration parameters. First, a dense 3D point cloud and the associated relative camera parameters are extracted from the optical aerial imagery using a state-of-the-art 3D reconstruction algorithm. Next, a digital surface model (DSM) is generated from both the LiDAR and the optical imagery-derived point clouds. Coarse registration parameters are then computed from salient features extracted from the LiDAR and optical imagery-derived DSMs. The registration parameters are further refined using the iterative closest point (ICP) algorithm to minimize global error between the registered point clouds. The novelty of the proposed approach is in the computation of salient features from the DSMs, and the selection of matching salient features using geometric invariants coupled with Normalized Cross Correlation (NCC) match validation. The feature extraction and matching process enables the automatic estimation of the coarse registration parameters required for initializing the fine registration process. The registration framework is tested on a simulated scene and aerial datasets acquired in real urban environments. Results demonstrates the robustness of the framework for registering optical and 3D structural information extracted from aerial imagery to a LiDAR point cloud, when co-existing initial registration parameters are unavailable. Numéro de notice : A2015-722 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.05.006 En ligne : https://doi.org/10.1016/j.isprsjprs.2015.05.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78369 [article]