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Visual servoing from deep neural networks
- Source :
- Proceedings of the Deep Learning Workshop at Robotics: Science and Systems Conference 2017
- Publication Year :
- 2017
-
Abstract
- We present a deep neural network-based method to perform high-precision, robust and real-time 6 DOF visual servoing. The paper describes how to create a dataset simulating various perturbations (occlusions and lighting conditions) from a single real-world image of the scene. A convolutional neural network is fine-tuned using this dataset to estimate the relative pose between two images of the same scene. The output of the network is then employed in a visual servoing control scheme. The method converges robustly even in difficult real-world settings with strong lighting variations and occlusions.A positioning error of less than one millimeter is obtained in experiments with a 6 DOF robot.
Details
- Database :
- OAIster
- Journal :
- Proceedings of the Deep Learning Workshop at Robotics: Science and Systems Conference 2017
- Notes :
- application/pdf
- Publication Type :
- Electronic Resource
- Accession number :
- edsoai.on1146608103
- Document Type :
- Electronic Resource