Multi-view self-supervised deep learning for 6D pose estimation in the Amazon Picking Challenge
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1609.09475.pdf
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Author(s)
Zeng, Andy
Song, Shuran
Suo, Daniel
Walker, Ed
Xiao, Jianxiong
Yu, Kuan-Ting
Rodriguez Garcia, Alberto
Date Issued
July 2017
Journal
2017 IEEE International Conference on Robotics and Automation (ICRA)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Zeng, Andy, Kuan-Ting Yu, Shuran Song, Daniel Suo, Ed Walker, Alberto Rodriguez, and Jianxiong Xiao. “Multi-View Self-Supervised Deep Learning for 6D Pose Estimation in the Amazon Picking Challenge.” 2017 IEEE International Conference on Robotics and Automation (ICRA), 29 May - 3 July, 2017, Singapore, Singapore, IEEE, 2017. © 2017 IEEE
Version
Author's final manuscript
Abstract
Robot warehouse automation has attracted significant interest in recent years, perhaps most visibly in the Amazon Picking Challenge (APC) [1]. 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 multiview 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/
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Mechanical Engineering
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/ICRA.2017.7989165
