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SegICP: Integrated Deep Semantic Segmentation and Pose Estimation
- Publication Year :
- 2017
-
Abstract
- 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 1cm position error and <5^\circ$ angle error in real time without an initial seed. We evaluate and benchmark SegICP against an annotated dataset generated by motion capture.<br />Comment: IROS camera-ready
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.1703.01661
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1109/IROS.2017.8206470