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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]