Fraunhofer-Gesellschaft

Publica

Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

Large-scale 6D Object Pose Estimation Dataset for Industrial Bin-Picking

 
: Kleeberger, Kilian; Landgraf, Christian; Huber, Marco

:

Institute of Electrical and Electronics Engineers -IEEE-:
IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019 : 3-8 November 2019, Macau, China
Piscataway, NJ: IEEE, 2019
ISBN: 978-1-7281-4004-9
ISBN: 978-1-7281-4003-2
ISBN: 978-1-7281-4005-6
pp.2573-2578
International Conference on Intelligent Robots and Systems (IROS) <2019, Macau>
English
Conference Paper
Fraunhofer IPA ()
bin-picking; Künstliche Intelligenz; neuronales Netz; Robotik

Abstract
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 comprising the 6D pose (position and orientation), a visibility score, and a segmentation mask for each object are provided. Along with the raw data, a method for precisely annotating real-world scenes is proposed.
To the best of our knowledge, this is the first public dataset for 6D object pose estimation and instance segmentation for bin-picking containing sufficiently annotated data for learning based approaches. Furthermore, it is one of the largest public datasets for object pose estimation in general. The dataset is publicly available at http://www.bin-picking.ai/en/dataset.html.

: http://publica.fraunhofer.de/documents/N-577938.html