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  5. SymmeTac: Symmetric Color LED Driven Efficient Photometric Stereo Reconstruction Methods for Camera-based Tactile Sensors

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Preprint
English
2024

SymmeTac: Symmetric Color LED Driven Efficient Photometric Stereo Reconstruction Methods for Camera-based Tactile Sensors

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0 Files

English
2024
arXiv (Cornell University)
DOI: 10.48550/arxiv.2411.06377

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Guoying Gu
Guoying Gu

Shanghai Jiao Tong University

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Jieji Ren
Heng Guo
Zaiyan Yang
+6 more

Abstract

Camera-based tactile sensors can provide high-density surface geometry and force information for robots in the interaction process with the target. However, most existing methods cannot achieve accurate reconstruction with high efficiency, impeding the applications in robots. To address these problems, we propose an efficient two-shot photometric stereo method based on symmetric color LED distribution. Specifically, based on the sensing response curve of CMOS channels, we design orthogonal red and blue LEDs as illumination to acquire four observation maps using channel-splitting in a two-shot manner. Subsequently, we develop a two-shot photometric stereo theory, which can estimate accurate surface normal and greatly reduce the computing overhead in magnitude. Finally, leveraging the characteristics of the camera-based tactile sensor, we optimize the algorithm to be a highly efficient, pure addition operation. Simulation and real-world experiments demonstrate the advantages of our approach. Further details are available on: https://github.com/Tacxels/SymmeTac.

How to cite this publication

Jieji Ren, Heng Guo, Zaiyan Yang, Jinnuo Zhang, Yunlong Dong, Ningbin Zhang, Boxin Shi, Jiang Zou, Guoying Gu (2024). SymmeTac: Symmetric Color LED Driven Efficient Photometric Stereo Reconstruction Methods for Camera-based Tactile Sensors. arXiv (Cornell University), DOI: 10.48550/arxiv.2411.06377.

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Publication Details

Type

Preprint

Year

2024

Authors

9

Datasets

0

Total Files

0

Language

English

Journal

arXiv (Cornell University)

DOI

10.48550/arxiv.2411.06377

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