Authors:
Tian Xu
and
Paul Cockshott
Affiliation:
University of Glasgow, United Kingdom
Keyword(s):
Stereo Matching, Robotic Vision, Foveated Matching, Cloth Manipulation, Grasp, Flatten, Evaluation, GPU.
Abstract:
Due to the recent development of robotic techniques, cloth manipulation has become an important task. Stereo matching forms a crucial part of the robotic vision and aims to derive the depth information from the image pairs captured by the stereo cameras. However, processing high resolution images to capture sufficient details meanwhile in real-time is very challenging. In addition to accelerating by current multi-core GPU infrastructure, in this work, we utilize foveated matching algorithm to improve the efficiency. To study the effect of foveated matching algorithm on two common robotic manipulation tasks, cloth grasping and flattening, we first create a "garment with wrinkle" dataset that includes depth map ground-truth for garments, which is to our knowledge not available in the research community. Secondly, using this dataset, we found that foveated matching is effective in trading off accuracy for efficiency for stereo matching. Finally, by assuming the robotic behavior followin
g previous work for both cloth grasping and flattening tasks, we demonstrate that using foveated matching can achieve the same level of accuracy for completing both tasks with two to three times of acceleration.
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