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A Grasp Pose Detection Network Based on the DeepLabv3+ Semantic Segmentation Model

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Intelligent Robotics and Applications (ICIRA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13458))

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Abstract

Gras** is an important and fundamental action for the interaction between robots and the environment. However, because gras** is a complex system engineering, there is still much room for development. At present, many studies use regression to solve the problem of grasp detection or use the unstable 3D point cloud as input, which may cause poor results to a certain extent. In this paper, we propose to use semantic segmentation of pixel-level classification to solve the problem of grasp pose detection. We adopt a grasp detection method based on the DeepLabv3+ model, which includes semantic segmentation and post-processing. In the semantic segmentation part, the classification mask of the objects is predicted through the input RGB image, and then the predicted objects of different classifications are fitted with the minimum bounding directed rectangle to obtain the two-dimensional grasp pose, and the final three-dimensional gras** pose is calculated through the conversion of the input depth image. On the validation dataset, we use the indicator of semantic segmentation to evaluate the proposed network and achieve a great result. In addition, the simulation robot experiment further verifies the effectiveness of the network.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant No. 62003048) and the National Key Research and Development Program of China (Grant No. 2019YFB1309802).

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Authors and Affiliations

  1. Bei**g Information Science and Technology University, Bei**g, China

    Qinjian Zhang & **angyan Zhang

  2. Bei**g University of Posts and Telecommunications, Bei**g, China

    Haiyuan Li

Authors
  1. Qinjian Zhang
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  2. **angyan Zhang
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  3. Haiyuan Li
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Corresponding author

Correspondence to Haiyuan Li .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology, Shenzhen, China

    Honghai Liu

  2. Huazhong University of Science and Technology, Wuhan, China

    Zhou** Yin

  3. Shenyang Institute of Automation, Shenyang, Liaoning, China

    Lianqing Liu

  4. Harbin Institute of Technology, Harbin, China

    Li Jiang

  5. Shanghai Jiao Tong University, Shanghai, China

    Guoying Gu

  6. Shenzhen Institute of Advanced Technology, Shenzhen, China

    **nyu Wu

  7. Harbin Institute of Technology, Shenzhen, China

    Weihong Ren

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Zhang, Q., Zhang, X., Li, H. (2022). A Grasp Pose Detection Network Based on the DeepLabv3+ Semantic Segmentation Model. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13458. Springer, Cham. https://doi.org/10.1007/978-3-031-13841-6_67

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  • DOI: https://doi.org/10.1007/978-3-031-13841-6_67

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-13840-9

  • Online ISBN: 978-3-031-13841-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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