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Occupancy modelling for moving object detection from Lidar point clouds: A comparative study / Wen Xiao in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol IV-2/W4 (September 2017)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol IV-2/W4 (September 2017) . - pp 171 - 178 Titre : Occupancy modelling for moving object detection from Lidar point clouds: A comparative study Type de document : Article/Communication Auteurs : Wen Xiao, Auteur ; Bruno Vallet , Auteur ; Y. Xiao, Auteur ; Jon Mills, Auteur ; Nicolas Paparoditis , Auteur Année de publication : 2017 Article en page(s) : pp 171 - 178 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] détection d'objet [Termes IGN] détection de changement [Termes IGN] données lidar [Termes IGN] grille [Termes IGN] objet mobile [Termes IGN] semis de points [Termes IGN] théorie de Dempster-Shafer Résumé : (auteur) Lidar technology has been widely used in both robotics and geomatics for environment perception and mapping. Moving object detection is important in both fields as it is a fundamental step for collision avoidance, static background extraction, moving pattern analysis, etc. A simple method involves checking directly the distance between nearest points from the compared datasets. However, large distances may be obtained when two datasets have different coverages. The use of occupancy grids is a popular approach to overcome this problem. There are two common theories employed to model occupancy and to interpret the measurements, DempsterShafer theory and probability. This paper presents a comparative study of these two theories for occupancy modelling with the aim of moving object detection from lidar point clouds. Occupancy is modelled using both approaches and their implementations are explained and compared in details. Two lidar datasets are tested to illustrate the moving object detection results Numéro de notice : A2017-913 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-IV-2-W4-171-2017 En ligne : https://doi.org/10.5194/isprs-annals-IV-2-W4-171-2017 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102874 [article]