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Semi-Automatic Generation of Training Data for Neural Networks for 6D Pose Estimation and Robotic Grasping
2020
Zenodo
Machine-learning-based approaches for pose estimation are trained using annotated groundtruth data – images showing the object and information of its pose. In this work an approach to semiautomatically generate 6D pose-annotated data, using a movable marker and an articulated robot, is presented. A neural network for pose estimation is trained using datasets varying in size and type. The evaluation shows that small datasets recorded in the target domain and supplemented with augmented images
doi:10.5281/zenodo.4084908
fatcat:7fscsh6qwbah5iyvfj3a5tsqty