论文标题
在工业环境中有效部署CNN进行3DOF姿势估算和抓地力
Effective Deployment of CNNs for 3DoF Pose Estimation and Grasping in Industrial Settings
论文作者
论文摘要
在本文中，我们调查了如何在实用的工业环境中有效地部署深度学习，例如机器人握把应用。当提出基于深度学习的解决方案时，通常缺乏生成训练数据的任何简单方法。在工业领域，自动化是主要目标，而不是弥合此差距是深度学习不像学术界那样普遍的主要原因之一。因此，在这项工作中，我们开发了一种由基于卷积神经网络（CNN）的3-DOF姿势估计量组成的系统，以及一种有效的程序，以最少的人为干预，在现场收集大量训练图像。通过自动化标签阶段，我们还获得了适合生产级使用的非常健壮的系统。还提供了我们解决方案的开源实现，以及用于实验评估的数据集。
In this paper we investigate how to effectively deploy deep learning in practical industrial settings, such as robotic grasping applications. When a deep-learning based solution is proposed, usually lacks of any simple method to generate the training data. In the industrial field, where automation is the main goal, not bridging this gap is one of the main reasons why deep learning is not as widespread as it is in the academic world. For this reason, in this work we developed a system composed by a 3-DoF Pose Estimator based on Convolutional Neural Networks (CNNs) and an effective procedure to gather massive amounts of training images in the field with minimal human intervention. By automating the labeling stage, we also obtain very robust systems suitable for production-level usage. An open source implementation of our solution is provided, alongside with the dataset used for the experimental evaluation.
