Visual-Tactile Fusion for Object Recognition

The camera provides rich visual information regarding objects and becomes one of the most mainstream sensors in the automation community. However, it is often difficult to be applicable when the objects are not visually distinguished. On the other hand, tactile sensors can be used to capture multipl...

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Veröffentlicht in:	IEEE transactions on automation science and engineering Jg. 14; H. 2; S. 996 - 1008
Hauptverfasser:	Huaping Liu, Yuanlong Yu, Fuchun Sun, Gu, Jason
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	IEEE 01.04.2017
Schlagworte:	Automation Joint sparse coding Manipulators Object recognition tactile perception Tactile sensors Training visual perception Visualization
ISSN:	1545-5955, 1558-3783
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Abstract	The camera provides rich visual information regarding objects and becomes one of the most mainstream sensors in the automation community. However, it is often difficult to be applicable when the objects are not visually distinguished. On the other hand, tactile sensors can be used to capture multiple object properties, such as textures, roughness, spatial features, compliance, and friction, and therefore provide another important modality for the perception. Nevertheless, effective combination of the visual and tactile modalities is still a challenging problem. In this paper, we develop a visual-tactile fusion framework for object recognition tasks. This paper uses the multivariate-time-series model to represent the tactile sequence and the covariance descriptor to characterize the image. Further, we design a joint group kernel sparse coding (JGKSC) method to tackle the intrinsically weak pairing problem in visual-tactile data samples. Finally, we develop a visual-tactile data set, composed of 18 household objects for validation. The experimental results show that considering both visual and tactile inputs is beneficial and the proposed method indeed provides an effective strategy for fusion.
AbstractList	The camera provides rich visual information regarding objects and becomes one of the most mainstream sensors in the automation community. However, it is often difficult to be applicable when the objects are not visually distinguished. On the other hand, tactile sensors can be used to capture multiple object properties, such as textures, roughness, spatial features, compliance, and friction, and therefore provide another important modality for the perception. Nevertheless, effective combination of the visual and tactile modalities is still a challenging problem. In this paper, we develop a visual-tactile fusion framework for object recognition tasks. This paper uses the multivariate-time-series model to represent the tactile sequence and the covariance descriptor to characterize the image. Further, we design a joint group kernel sparse coding (JGKSC) method to tackle the intrinsically weak pairing problem in visual-tactile data samples. Finally, we develop a visual-tactile data set, composed of 18 household objects for validation. The experimental results show that considering both visual and tactile inputs is beneficial and the proposed method indeed provides an effective strategy for fusion.
Author	Gu, Jason Fuchun Sun Huaping Liu Yuanlong Yu
Author_xml	– sequence: 1 surname: Huaping Liu fullname: Huaping Liu email: hpliu@tsinghua.edu.cn organization: Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing, China – sequence: 2 surname: Yuanlong Yu fullname: Yuanlong Yu email: yu.yuanlong@fzu.edu.cn organization: Coll. of Comput., Fuzhou Univ., Fuzhou, China – sequence: 3 surname: Fuchun Sun fullname: Fuchun Sun email: fcsun@tsinghua.edu.cn organization: Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing, China – sequence: 4 givenname: Jason surname: Gu fullname: Gu, Jason email: jason.gu@dal.ca organization: Dept. of Electr. & Comput. Eng., Dalhousie Univ., Halifax, NS, Canada
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Snippet	The camera provides rich visual information regarding objects and becomes one of the most mainstream sensors in the automation community. However, it is often...
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SubjectTerms	Automation Joint sparse coding Manipulators Object recognition tactile perception Tactile sensors Training visual perception Visualization
Title	Visual-Tactile Fusion for Object Recognition
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