Localizing an object accurately with respect to a robot is a key step for autonomous robotic manipulation. In this work, we propose to tackle this task knowing only 3D models of the robot and object in the particular case where the scene is viewed from uncalibrated cameras—a situation which would be typical in an uncontrolled environment, e.g., on a construction site. We demonstrate that this localization can be performed very accurately, with millimetric errors, without using a single real image for training, a strong advantage since acquiring representative training data is a long and expensive process. Our approach relies on a classification Convolutional Neural Network trained using hundreds of thousands of synthetically rendered scenes with randomized parameters. To evaluate our approach quantitatively and make it comparable to alternative approaches, we build a new rich dataset of real robot images with accurately localized blocks.

中文翻译：

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实际应用的虚拟训练：无需校准的准确对象-机器人相对定位

相对于机器人准确定位对象是自主机器人操作的关键步骤。在这项工作中，我们建议在特定情况下仅知道机器人和物体的 3D 模型来解决此任务，即从未校准的相机查看场景——这种情况在不受控制的环境中是典型的，例如在建筑工地。我们证明了这种定位可以非常准确地执行，具有毫米级误差，无需使用单个真实图像进行训练，这是一个强大的优势，因为获取代表性训练数据是一个漫长而昂贵的过程。我们的方法依赖于使用数十万个具有随机参数的合成渲染场景训练的分类卷积神经网络。