Name
Abstract | ||
|---|---|---|
End-effectors are usually related to the location of the free end of a kinematic chain. Each of them contains rich structure information about the entity. Hence, estimating stable end-effectors of different entities enables robust tracking as well as a generic representation. In this paper, we present a system for end-effector estimation from RGB-D stream data. Instead of relying on a specific pose or configuration for initialization, we exploit time coherence without making any assumption with respect to the prior knowledge. This makes the estimation process more robust in a predict-update framework. Qualitative and quantitative experiments are performed against the reference method with promising results. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-29451-3_42 | PSIVT |
Keywords | Field | DocType |
End-effector estimation, Time coherence, Topology representation | Consistent Estimation,Computer vision,Pattern recognition,Computer science,Image processing,Coherence (physics),Exploit,Robot end effector,RGB color model,Artificial intelligence,Initialization,Kinematic chain | Conference |
Volume | ISSN | Citations |
9431 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiao Lin | 1 | 0 | 1.35 |
| Josep R. Casas | 2 | 212 | 23.34 |
| Montse Pardàs | 3 | 343 | 35.03 |