Name
Abstract | ||
|---|---|---|
Vision-based grasping for humanoid robots is a challenging problem due to a multitude of factors. First, humanoid robots use an “eye-to-hand” kinematics configuration that, on the contrary to the more common “eye-in-hand” configuration, demands a precise estimate of the position of the robotu0027s hand. Second, humanoid robots have a long kinematic chain from the eyes to the hands, prone to accumulate the calibration errors of the kinematics model, which offsets the measured hand-to-object relative pose from the real one. In this paper, we propose a method able to solve these two issues jointly. A robust pose estimation of the robotu0027s hand is achieved via a 3D model-based stereo-vision algorithm, using an edge-based distance transform metric and synthetically generated images of a robotu0027s arm-hand internal computer-graphics model (kinematics and appearance). Then, a particle-based optimization method adapts on-line the robotu0027s internal model to match the real and the synthetically generated images, effectively compensating the kinematics calibration errors. We evaluate the proposed approach using a position-based visual-servoing method on the iCub robot, showing the importance of the continuous visual feedback in humanoid grasping tasks. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICRA.2017.7989441 | ICRA |
Field | DocType | Volume |
Robot control,Computer vision,iCub,Kinematics,Robot kinematics,Pose,Visual servoing,Artificial intelligence,Engineering,Robot,Humanoid robot | Conference | 2017 |
Issue | Citations | PageRank |
1 | 0 | 0.34 |
References | Authors | |
19 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vicente, P. | 1 | 15 | 5.46 |
| Lorenzo Jamone | 2 | 149 | 20.57 |
| Alexandre Bernardino | 3 | 710 | 78.77 |