SegICP: Integrated deep semantic segmentation and pose estimation

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 s...

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Bibliographic Details
Main Authors: Wong, Jay M., Kee, Vincent, Le, Tiffany, Wagner, Syler, Mariottini, Gian-Luca, Schneider, Abraham, Hamilton, Lei, Chipalkatty, Rahul, Hebert, Mitchell, Johnson, David M. S., Wu, Jimmy, Bolei Zhou, Torralba, Antonio
Format: Conference Proceeding
Language:English
Subjects:
Agriculture
Measurement
Pose estimation
Robots
Robustness
Semantics
Three-dimensional displays
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Summary: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
ISSN:2153-0866
DOI:10.1109/IROS.2017.8206470