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Robot warehouse automation has attracted significant interest in recentyears, perhaps most visibly in the Amazon Picking Challenge (APC). A fullyautonomous warehouse pick-and-place system requires robust vision that reliablyrecognizes and locates objects amid cluttered environments, self-occlusions,sensor noise, and a large variety of objects. In this paper we present anapproach that leverages multi-view RGB-D data and self-supervised, data-drivenlearning to overcome those difficulties. The approach was part of theMIT-Princeton Team system that took 3rd- and 4th- place in the stowing andpicking tasks, respectively at APC 2016. In the proposed approach, we segmentand 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 getthe 6D object pose. Training a deep neural network for segmentation typicallyrequires a large amount of training data. We propose a self-supervised methodto generate a large labeled dataset without tedious manual segmentation. Wedemonstrate that our system can reliably estimate the 6D pose of objects undera variety of scenarios. All code, data, and benchmarks are available athttp://apc.cs.princeton.edu/

Multi-view Self-supervised Deep Learning for 6D Pose Estimation in the Amazon Picking Challenge