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17 September 2019 Efficient 3D object tracking approach based on convolutional neural network and Monte Carlo algorithms used for a pick and place robot
Y. Zhang, C. Zhang, R. Nestler, M. Rosenberger, G. Notni
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Proceedings Volume 11144, Photonics and Education in Measurement Science 2019; 1114414 (2019) https://doi.org/10.1117/12.2530333
Event: Joint TC1 - TC2 International Symposium on Photonics and Education in Measurement Science 2019, 2019, Jena, Germany
Abstract
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.
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Y. Zhang, C. Zhang, R. Nestler, M. Rosenberger, and G. Notni "Efficient 3D object tracking approach based on convolutional neural network and Monte Carlo algorithms used for a pick and place robot", Proc. SPIE 11144, Photonics and Education in Measurement Science 2019, 1114414 (17 September 2019); https://doi.org/10.1117/12.2530333
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KEYWORDS
3D image processing
Convolutional neural networks
Evolutionary algorithms
Image processing
Monte Carlo methods
Image segmentation
Neural networks
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