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Syler Wagner

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2 papers

1 author row

IROS Conference 2018 Conference Paper

Real-Time Object Pose Estimation with Pose Interpreter Networks

Jimmy Wu
Bolei Zhou
Rebecca L. Russell
Vincent Kee
Syler Wagner
Mitchell Hebert
Antonio Torralba 0001
David M. S. Johnson

In this work, we introduce pose interpreter networks for 6-DoF object pose estimation. In contrast to other CNN-based approaches to pose estimation that require expensively annotated object pose data, our pose interpreter network is trained entirely on synthetic pose data. We use object masks as an intermediate representation to bridge real and synthetic. We show that when combined with a segmentation model trained on RGB images, our synthetically trained pose interpreter network is able to generalize to real data. Our end-to-end system for object pose estimation runs in real-time (20 Hz) on live RGB data, without using depth information or ICP refinement.

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IROS Conference 2017 Conference Paper

SegICP: Integrated deep semantic segmentation and pose estimation

Jay Ming Wong
Vincent Kee
Tiffany Le
Syler Wagner
Gian Luca Mariottini
Abraham Schneider
Lei Hamilton
Rahul Chipalkatty

Recent robotic manipulation competitions have highlighted that sophisticated robots still struggle to achieve fast and reliable perception of task-relevant objects in complex, realistic scenarios. To improve these systems' perceptive speed and robustness, we present SegICP, a novel integrated solution to object recognition and pose estimation. SegICP couples convolutional neural networks and multi-hypothesis point cloud registration to achieve both robust pixel-wise semantic segmentation as well as accurate and real-time 6-DOF pose estimation for relevant objects. Our architecture achieves 1 cm position error and <; 5° angle error in real time without an initial seed. We evaluate and benchmark SegICP against an annotated dataset generated by motion capture.

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