Active Tactile Exploration With Emphasis on 3D Texture Modeling

Active tactile exploration effectively reconstructs 3D shapes, particularly when visual input is limited. However, current Gaussian process (GP)-based methods primarily capture global shapes, often neglecting local texture details. To overcome the limitation, a texture-guided active tactile exploration pipeline is proposed for accurate texture reconstruction. Specifically, the GelStereo Palm 2.0 sensor is leveraged to collect high-precision tactile point clouds. GP is used to model object shapes, complemented by a GP classifier to learn the distribution of texture regions based on a local texture intensity indicator. By integrating outputs from the regression model and the classifier, the pipeline devises effective exploration strategies. Experiments on different wave surfaces and irregular objects demonstrate that our method achieves a 15% accuracy and 42% speed increase in reconstructing texture region over baseline methods. This technique shows great potential for applications in underwater shape perception and the reconstruction of transparent objects. See supplementary video and results at https://sites.google.com/ view/activetactileexploration.

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Publication Details

Journal
IEEE Transactions on Instrumentation and Measurement
Published
2025-01-01
DOI
https://doi.org/10.1109/tim.2025.3561402
Primary Topic
Industrial Vision Systems and Defect Detection
Type
article
Field-Weighted Citation Impact
0.00

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article

Active Tactile Exploration With Emphasis on 3D Texture Modeling

Shaowei Cui, Rui Wang, Shuo Wang, Jingyi Hu
IEEE Transactions on Instrumentation and Measurement
Industrial Vision Systems and Defect Detection
article

Active Tactile Exploration With Emphasis on 3D Texture Modeling

Shaowei Cui, Rui Wang, Shuo Wang, Jingyi Hu
article en

Abstract

Active tactile exploration effectively reconstructs 3D shapes, particularly when visual input is limited. However, current Gaussian process (GP)-based methods primarily capture global shapes, often neglecting local texture details. To overcome the limitation, a texture-guided active tactile exploration pipeline is proposed for accurate texture reconstruction. Specifically, the GelStereo Palm 2.0 sensor is leveraged to collect high-precision tactile point clouds. GP is used to model object shapes, complemented by a GP classifier to learn the distribution of texture regions based on a local texture intensity indicator. By integrating outputs from the regression model and the classifier, the pipeline devises effective exploration strategies. Experiments on different wave surfaces and irregular objects demonstrate that our method achieves a 15% accuracy and 42% speed increase in reconstructing texture region over baseline methods. This technique shows great potential for applications in underwater shape perception and the reconstruction of transparent objects. See supplementary video and results at https://sites.google.com/ view/activetactileexploration.

IEEE Transactions on Instrumentation and MeasurementVol. 74
Chinese Academy of Sciences (CN), Institute of Automation (CN), University of Chinese Academy of Sciences (CN)
National Natural Science Foundation of China, Natural Science Foundation of Beijing Municipality, National Key Research and Development Program of China
Openalex Percentile: Top 90%
Industrial Vision Systems and Defect Detection
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