多視点自己教師あり学習による6D姿勢推定：Amazon Picking Challengeへの応用
Multi-view Self-supervised Deep Learning for 6D Pose Estimation in the Amazon Picking Challenge
多視点RGB-Dデータと自己教師あり学習を組み合わせ、 clutter環境でも物体の6D姿勢を高精度に推定する手法を提案し、APC 2016で有効性を示した。
著者: Andy Zeng, Kuan-Ting Yu, Shuran Song, Daniel Suo, Ed Walker, Alberto Rodriguez, Jianxiong Xiao
分類: cs.CV, cs.LG, cs.RO
原文アブストラクト
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 http://apc.cs.princeton.edu/
