Grasp area detection of unknown objects based on deep semantic segmentation
2020 IEEE 16th International Conference on Automation Science and …, 2020•ieeexplore.ieee.org
This paper presents a novel approach to grasp detection of unknown items with Deep
Semantic Segmentation. The approach can detect whether an object is occluded or
graspable and offers the possibility to determine grasp vectors for several grippers. This is
accomplished with the help of Semantic Segmentation. The Segmentation is tested with
Fully Convolutional Networks (FCN)[1] and DeepLab V3+ [2]. The neural networks use RGB
images, depth images or both. The size, speed and functionality of the neural networks with …
Semantic Segmentation. The approach can detect whether an object is occluded or
graspable and offers the possibility to determine grasp vectors for several grippers. This is
accomplished with the help of Semantic Segmentation. The Segmentation is tested with
Fully Convolutional Networks (FCN)[1] and DeepLab V3+ [2]. The neural networks use RGB
images, depth images or both. The size, speed and functionality of the neural networks with …
This paper presents a novel approach to grasp detection of unknown items with Deep Semantic Segmentation. The approach can detect whether an object is occluded or graspable and offers the possibility to determine grasp vectors for several grippers. This is accomplished with the help of Semantic Segmentation. The Segmentation is tested with Fully Convolutional Networks (FCN) [1] and DeepLab V3+ [2]. The neural networks use RGB images, depth images or both. The size, speed and functionality of the neural networks with different inputs are discussed. A determination for grasp vectors for parallel grippers is explained and experiments are carried out. In contrast to current methods, which only calculate a single grasp vector, our approach can determine all possible grasp vectors.
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