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In this paper, a new method based on deep learning for robotics autonomous navigation is presented. Different from the most traditional methods based on fixed models, a convolutional neural network (CNN) modelling technique in Deep learning is selected to extract the feature inspired by the working pattern of the biological brain. This neural network model has muti-layer features where the ambient...
This paper addresses a 3D object recognition and pose estimation method with a deep learning model. We train two separated Deep Belief Networks (DBN) before connecting the last layers together to train a classifier. By this means, we can simplify the complicated 3D problem to an easier classifier training problem. The deep learning model shows its advantages in learning hierarchical features which...
Pose estimation of object is one of the key problems for the automatic-grasping task of robotics. In this paper, we present a new vision-based robotic grasping system, which can not only recognize different objects but also estimate their poses by using a deep learning model, finally grasp them and move to a predefined destination. The deep learning model demonstrates strong power in learning hierarchical...
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