Name
Abstract | ||
|---|---|---|
Robot manipulation tasks like inserting screws and pegs into a hole or automatic screwing require precise tip pose estimation. We propose a novel method to detect and estimate the tip of elongated objects. We demonstrate that our method can estimate tip pose to millimeter-level accuracy. We adopt a probabilistic, appearance-based object detection framework to detect pegs and bits for electric screw drivers. Screws are difficult to detect with feature- or appearance-based methods due to their reflective characteristics. To overcome this we propose a novel adaptation of RANSAC with a parallel-line model. Subsequently, we employ image moments to detect the tip and its pose. We show that the proposed method allows a robot to perform object insertion with only two pairs of orthogonal views, without visual servoing. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-20904-3_33 | ICVS |
Keywords | Field | DocType |
Pose estimation, Tool tip detection, Peg-in-hole insertion | Computer vision,Object detection,RANSAC,Computer science,3D pose estimation,Pose,Visual servoing,Artificial intelligence,Probabilistic logic,Robot,Image moment | Conference |
Volume | ISSN | Citations |
9163 | 0302-9743 | 1 |
PageRank | References | Authors |
0.39 | 10 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dadhichi Shukla | 1 | 21 | 3.11 |
| Özgür Erkent | 2 | 26 | 4.96 |
| Justus H. Piater | 3 | 543 | 61.56 |