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pro vyhledávání: '"Kleeberger, Kilian"'
For various automated palletizing tasks, the detection of packaging units is a crucial step preceding the actual handling of the packaging units by an industrial robot. We propose an approach to this challenging problem that is fully trained on synth
Externí odkaz:
http://arxiv.org/abs/2308.06306
Autor:
Kleeberger, Kilian, Schnitzler, Jonathan, Khalid, Muhammad Usman, Bormann, Richard, Kraus, Werner, Huber, Marco F.
This paper introduces a novel approach for the grasping and precise placement of various known rigid objects using multiple grippers within highly cluttered scenes. Using a single depth image of the scene, our method estimates multiple 6D object pose
Externí odkaz:
http://arxiv.org/abs/2110.00992
This paper presents a novel approach for the automatic offline grasp pose synthesis on known rigid objects for parallel jaw grippers. We use several criteria such as gripper stroke, surface friction, and a collision check to determine suitable 6D gra
Externí odkaz:
http://arxiv.org/abs/2104.11660
Single shot approaches have demonstrated tremendous success on various computer vision tasks. Finding good parameterizations for 6D object pose estimation remains an open challenge. In this work, we propose different novel parameterizations for the o
Externí odkaz:
http://arxiv.org/abs/2104.07528
Autor:
Kleeberger, Kilian, Völk, Markus, Moosmann, Marius, Thiessenhusen, Erik, Roth, Florian, Bormann, Richard, Huber, Marco F.
In this paper, we introduce a novel learning-based approach for grasping known rigid objects in highly cluttered scenes and precisely placing them based on depth images. Our Placement Quality Network (PQ-Net) estimates the object pose and the quality
Externí odkaz:
http://arxiv.org/abs/2101.04781
Autor:
Kleeberger, Kilian, Huber, Marco F.
In this paper, we introduce a novel single shot approach for 6D object pose estimation of rigid objects based on depth images. For this purpose, a fully convolutional neural network is employed, where the 3D input data is spatially discretized and po
Externí odkaz:
http://arxiv.org/abs/2004.12729
In this paper, we introduce a new public dataset for 6D object pose estimation and instance segmentation for industrial bin-picking. The dataset comprises both synthetic and real-world scenes. For both, point clouds, depth images, and annotations com
Externí odkaz:
http://arxiv.org/abs/1912.12125
Autor:
Moosmann, Marius, Spenrath, Felix, Kleeberger, Kilian, Khalid, Muhammad Usman, Mönnig, Manuel, Rosport, Johannes, Bormann, Richard
Publikováno v:
In Procedia CIRP 2020 93:1212-1217
Eine individualisierte Produktion erfordert es, kleine Losgrößen wirtschaftlich zu fertigen. Maschinelles Lernen bietet hierfür eine Lösung: Roboterprogrammierung und Bildverarbeitung können damit deutlich vereinfacht und zugleich leistungsfähi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fed3473550a5aca0f15e4b5fa6f798ad
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