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Efficient 3D object tracking approach based on convolutional neural network and Monte Carlo algorithms used for a pick and place robot

: Zhang, Y.; Zhang, C.; Nestler, R.; Rosenberger, M.; Notni, G.

Fulltext ()

Rosenberger, M. ; Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.:
Photonics and Education in Measurement Science 2019 : 17-19 September 2019, Jena, Germany
Bellingham, WA: SPIE, 2019 (Proceedings of SPIE 11144)
ISBN: 978-1-5106-2981-3
ISBN: 978-1-5106-2982-0
Paper 1114414, 6 pp.
Conference "Photonics and Education in Measurement Science" <2019, Jena>
Conference Paper, Electronic Publication
Fraunhofer IOF ()

Currently, Deep Learning (DL) shows us powerful capabilities for image processing. But it cannot output the exact photometric process parameters and shows non-interpretable results. Considering such limitations, this paper presents a robot vision system based on Convolutional Neural Networks (CNN) and Monte Carlo algorithms. As an example to discuss about how to apply DL in industry. In the approach, CNN is used for preprocessing and offline tasks. Then the 6-DoF object position are estimated using a particle filter approach. Experiments will show that our approach is efficient and accurate. In future it could show potential solutions for human-machine collaboration systems.