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Deep-trained illumination-robust precision positioning for real-time manipulation of embedded objects

Abstract

Automated manipulation guided by a precision visual positioning system is realized based on a convolutional neural network (ConvNet) trained on coordinate-explicit target images. Whereas ConvNet has exhibited superior illumination adaptability, it is still possible that high-contrast illumination effects may reduce the success rate of visually guided manipulation in the factory. In this paper, an automated training data generation scheme is proposed to enhance the performance of the visual positioning ConvNet under high-contrast shadows cast by other objects. By collecting dozens of illumination patterns, illumination templates are created by the image-to-image translation GAN (pix2pix GAN). The templates are then applied to the basis photo of the target object to spawn multiple virtual images which vastly enrich the illumination diversity of the training set for the visual positioning ConvNet. Experiments were conducted to verify the effectiveness of the pix2pix GAN illumination generation module. Experimental results also showed that the positioning accuracy augmented by the illumination module reaches above 85%, while without the illumination-augmented training, the positioning accuracy is below 15%. Experiments on automated visual manipulation with a 5-DOF manipulator also confirmed the feasibility of adopting the proposed framework for real-time operations. With the illumination-augmented training, the manipulation success rate is above 90%; without it, the success rate is less than 40%. We provide the data used for training pix2pix GAN to generate the illumination templates at https://github.com/ntutindustry40/OneShot-II.

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Correspondence to Chih-Hung G. Li.

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Li, CH.G., Huang, YH. Deep-trained illumination-robust precision positioning for real-time manipulation of embedded objects. Int J Adv Manuf Technol 111, 2259–2276 (2020). https://doi.org/10.1007/s00170-020-06185-x

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Keywords

  • GAN
  • pix2pix
  • Convolutional neural network
  • Visual positioning
  • Illumination
  • Manipulation