2022/11/02 by Rutinowski, Jérôme, Youssef, Hazem, Gouda, Anas +2
#Computer Vision #Deep Learning #Ingenieurwissenschaften (DDC 620) #Object Segmentation #Objekt Segmentierung #Pose Estimation #Re-Identification #Re-Identifikation
paper · doi:10.2195/lj_proc_rutinowski_en_202211_01
This work describes three deep learning based computer vision approaches, that hold the potential to increase the degree of automation and the productivity of common warehousing procedures. These approaches will focus on: the re-identification of logistical entities, especially when entering and leaving the warehouse; the multi-view pose estimation of logistical entities to track and to localize them on the shop floor; and the category-agnostic segmentation of items in a bin for robotic grasping.