|Publisher DOI:||10.2195/lj_Proc_thiel_de_201811_01||Title:||Deep learning for 3D object recognition in logistics||Language:||English||Authors:||Thiel, Marko
|Issue Date:||30-Nov-2018||Source:||Logistics Journal 1 (2018): (2018-11-30)||Journal or Series Name:||Logistics journal||Abstract (english):||The reliable detection of objects in sensor data is a fundamental requirement for the autono-mization of logistic processes. Especially the recognition of objects in 3D sensor data is important for flexible autonomous applications. Deep learning represents the state of the art for object recognition in 2D image data. This article presents various current approaches to use deep learning for 3D object recognition. An essential feature of these approaches is the use of point clouds as input data, possibly after prior segmentation or conversion into voxel grids. Examples of applications in logistics are autonomous guided vehicles and order picking robots. The challenges for an application are a lack of training data, high computing requirements for real-time applications and an accuracy that is not yet sufficient. © 2018 Logistics Journal: Proceedings.||URI:||http://hdl.handle.net/11420/2190||ISSN:||1860-7977||Institute:||Technische Logistik W-6||Type:||(wissenschaftlicher) Artikel|
|Appears in Collections:||Publications without fulltext|
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