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Image-Based Visual Servoing of Rotationally Invariant Objects Using a U-Net Prediction | IEEE Conference Publication | IEEE Xplore

Image-Based Visual Servoing of Rotationally Invariant Objects Using a U-Net Prediction


Abstract:

In this article an image-based visual servoing for the armature of electric motors is presented. For a calibrated monocular eye-in-hand camera system our goal is to move ...Show More

Abstract:

In this article an image-based visual servoing for the armature of electric motors is presented. For a calibrated monocular eye-in-hand camera system our goal is to move the camera to the desired position with respect to the armature. For this purpose we minimize the error between a corresponding feature vector and a measured feature vector. In this paper we derived various features from the output of a U-Net. The variety leads to the fact that we can decouple the features in the control process. The prediction of the U-Net is stabilized by strong augmentation, an armature model and an adaptive digital zoom. We can show that our U-Net control approach converges and is robust against noise and multiple objects.
Date of Conference: 04-06 February 2021
Date Added to IEEE Xplore: 17 March 2021
ISBN Information:
Conference Location: Prague, Czech Republic

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