Robot warehouse automation has attracted significant interest in recent
years, perhaps most visibly in the Amazon Picking Challenge (APC). A fully
autonomous warehouse pick-and-place system requires robust vision that reliably
recognizes and locates objects amid cluttered environments, self-occlusions,
sensor noise, and a large variety of objects. In this paper we present an
approach that leverages multi-view RGB-D data and self-supervised, data-driven
learning to overcome those difficulties. The approach was part of the
MIT-Princeton Team system that took 3rd- and 4th- place in the stowing and
picking tasks, respectively at APC 2016. In the proposed approach, we segment
and label multiple views of a scene with a fully convolutional neural network,
and then fit pre-scanned 3D object models to the resulting segmentation to get
the 6D object pose. Training a deep neural network for segmentation typically
requires a large amount of training data. We propose a self-supervised method
to generate a large labeled dataset without tedious manual segmentation. We
demonstrate that our system can reliably estimate the 6D pose of objects under
a variety of scenarios. All code, data, and benchmarks are available at
this http URL

本文介绍了一种利用多视角 RGB-D 数据和自监督数据驱动学习的方法，通过全卷积神经网络分割和标记场景中的多个视角，并将预先扫描的 3D 物体模型适配到分割结果中，以获得 6D 物体位姿的方法。利用自监督方法生成大量标记数据集，最终实验结果表明该系统能够可靠地估计各种环境下对象的 6D 姿态。