Name
Abstract | ||
|---|---|---|
We propose a high efficient learning approach to estimating 6D (Degree of Freedom) pose of the textured or texture-less objects for grasping purposes in a cluttered environment where the objects might be partially occluded. The method comprises three main steps. Given a single RGB-D image, we first deploy appropriate features and the random forest to deduce the object class probability and cast votes for the 6D pose in Hough space by joint regression and classification framework, adopting reservoir sampling and summarizing the pose distribution by clustering. Next, we integrate the auto-context into cascaded Hough forests to improve the efficiency of learning. Extensive experiments on various public datasets and robotic grasps indicate that our method presents some improvements over the state-of-art and reveals the capability for estimating poses in practical applications efficiently. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/IROS.2018.8594064 | 2018 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) |
Field | DocType | ISSN |
Computer vision,Degrees of freedom (statistics),Regression,Computer science,Reservoir sampling,Object Class,Pose,Artificial intelligence,RGB color model,Cluster analysis,Random forest | Conference | 2153-0858 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huixu Dong | 1 | 4 | 1.40 |
| Dilip K. Prasad | 2 | 162 | 21.84 |
| Qilong Yuan | 3 | 45 | 7.96 |
| Jiadong Zhou | 4 | 0 | 0.34 |
| Ehsan Asadi | 5 | 9 | 3.67 |
| I-Ming Chen | 6 | 567 | 87.28 |