arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2201.01367cs.ROcs.CV

DenseTact: Optical Tactile Sensor for Dense Shape Reconstruction

Stanford University(斯坦福大学)

Won Kyung Do, Monroe Kennedy

英文摘要:

Increasing the performance of tactile sensing in robots enables versatile, in-hand manipulation. Vision-based tactile sensors have been widely used as rich tactile feedback has been shown to be correlated with increased performance in manipulation tasks. Existing tactile sensor solutions with high resolution have limitations that include low accuracy, expensive components, or lack of scalability. In this paper, an inexpensive, scalable, and compact tactile sensor with high-resolution surface deformation modeling for surface reconstruction of the 3D sensor surface is proposed. By measuring the image from the fisheye camera, it is shown that the sensor can successfully estimate the surface deformation in real-time (1.8ms) by using deep convolutional neural networks. This sensor in its design and sensing abilities represents a significant step toward better object in-hand localization, classification, and surface estimation all enabled by high-resolution shape reconstruction.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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