SegICP: Integrated deep semantic segmentation and pose estimation

Jay M. Wong, Vincent Kee, Tiffany Le, Syler Wagner, Gian-Luca Mariottini, Abraham Schneider, Lei Hamilton, Rahul Chipalkatty, Mitchell Hebert, David M.S. Johnson, Jimmy Wu, Bolei Zhou (+1 others)
2017 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)  
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
more » ... as accurate and real-time 6-DOF pose estimation for relevant objects. Our architecture achieves 1cm 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.
doi:10.1109/iros.2017.8206470 dblp:conf/iros/WongKLWMSHCHJWZ17 fatcat:r7izr6hbw5efdpumwxfnbwrfc4