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A Fast 3D Object Recognition Pipeline in Cluttered and Occluded Scenes

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Intelligent Robotics and Applications (ICIRA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10463))

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Abstract

In this paper we propose a framework for instance recognition and object localization in cluttered and occluded household environment for robot grasping task. The whole system bases on a coarse to fine pipeline in combination with the state-of-the-art methods of RGBD-based object detection. We build a sparse feature model by extracting structure key points incorporating texture cues in the train procedure. After that, the paper demonstrates how the algorithm decreases the time complexity and simultaneously guarantees the accuracy of the recognition and pose estimation. Quantitative experimental evaluations are presented using both acknowledged ground truth dataset and real-world robot perception system.

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Acknowledgement

This work was supported in part by the Natural Science Foundation of China under Grant U1613218, 61473191 and 61503245, in part by the Science and Technology Commission of Shanghai Municipality under Grant 15111104802, in part by Shanghai Sailing Program under Grant 15YF1406300, in part by State Key Laboratory of Robotics and System (HIT).

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Authors and Affiliations

  1. Shanghai Jiao Tong University, Shanghai, 200240, China

    Liupo Zheng, Hesheng Wang & Weidong Chen

Authors
  1. Liupo Zheng
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  2. Hesheng Wang
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  3. Weidong Chen
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Corresponding author

Correspondence to Hesheng Wang .

Editor information

Editors and Affiliations

  1. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

    YongAn Huang

  2. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

    Hao Wu

  3. Institute of Industrial Research, University of Portsmouth, Portsmouth, United Kingdom

    Honghai Liu

  4. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

    Zhouping Yin

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Zheng, L., Wang, H., Chen, W. (2017). A Fast 3D Object Recognition Pipeline in Cluttered and Occluded Scenes. In: Huang, Y., Wu, H., Liu, H., Yin, Z. (eds) Intelligent Robotics and Applications. ICIRA 2017. Lecture Notes in Computer Science(), vol 10463. Springer, Cham. https://doi.org/10.1007/978-3-319-65292-4_51

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  • DOI: https://doi.org/10.1007/978-3-319-65292-4_51

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65291-7

  • Online ISBN: 978-3-319-65292-4

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