Visual-tactile Fusion for Transparent Object Grasping in Complex Backgrounds
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866913381621956608 |
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| author | Li, Shoujie Yu, Haixin Ding, Wenbo Liu, Houde Ye, Linqi Xia, Chongkun Wang, Xueqian Zhang, Xiao-Ping |
| author_facet | Li, Shoujie Yu, Haixin Ding, Wenbo Liu, Houde Ye, Linqi Xia, Chongkun Wang, Xueqian Zhang, Xiao-Ping |
| contents | The accurate detection and grasping of transparent objects are challenging but of significance to robots. Here, a visual-tactile fusion framework for transparent object grasping under complex backgrounds and variant light conditions is proposed, including the grasping position detection, tactile calibration, and visual-tactile fusion based classification. First, a multi-scene synthetic grasping dataset generation method with a Gaussian distribution based data annotation is proposed. Besides, a novel grasping network named TGCNN is proposed for grasping position detection, showing good results in both synthetic and real scenes. In tactile calibration, inspired by human grasping, a fully convolutional network based tactile feature extraction method and a central location based adaptive grasping strategy are designed, improving the success rate by 36.7% compared to direct grasping. Furthermore, a visual-tactile fusion method is proposed for transparent objects classification, which improves the classification accuracy by 34%. The proposed framework synergizes the advantages of vision and touch, and greatly improves the grasping efficiency of transparent objects. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2211_16693 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Visual-tactile Fusion for Transparent Object Grasping in Complex Backgrounds Li, Shoujie Yu, Haixin Ding, Wenbo Liu, Houde Ye, Linqi Xia, Chongkun Wang, Xueqian Zhang, Xiao-Ping Robotics Artificial Intelligence The accurate detection and grasping of transparent objects are challenging but of significance to robots. Here, a visual-tactile fusion framework for transparent object grasping under complex backgrounds and variant light conditions is proposed, including the grasping position detection, tactile calibration, and visual-tactile fusion based classification. First, a multi-scene synthetic grasping dataset generation method with a Gaussian distribution based data annotation is proposed. Besides, a novel grasping network named TGCNN is proposed for grasping position detection, showing good results in both synthetic and real scenes. In tactile calibration, inspired by human grasping, a fully convolutional network based tactile feature extraction method and a central location based adaptive grasping strategy are designed, improving the success rate by 36.7% compared to direct grasping. Furthermore, a visual-tactile fusion method is proposed for transparent objects classification, which improves the classification accuracy by 34%. The proposed framework synergizes the advantages of vision and touch, and greatly improves the grasping efficiency of transparent objects. |
| title | Visual-tactile Fusion for Transparent Object Grasping in Complex Backgrounds |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2211.16693 |