Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (11): 226-233.doi: 10.11896/jsjkx.201200095

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Optimization Method for Inter-frame Stability of Object Pose Estimation for Human-Machine Collaboration

MU Feng-jun1, QIU Jing1, CHEN Lu-feng2, HUANG Rui2, ZHOU Lin3, YU Gong-jing3   

  1. 1 School of Mechanical and Electrical Engineering,University of Electronic Science and Technology of China,Chengdu 611731,China
    2 School of Automation Engineering,University of Electronic Science and Technology of China,Chengdu 611731,China
    3 Innovation Center of Automated Testing for Science,Technology and Industry for National Defence,Beijing 100041,China
  • Received:2020-12-11 Revised:2021-03-14 Online:2021-11-15 Published:2021-11-10
  • About author:MU Feng-jun,born in 1997,postgra-duate.His main research interests include computer vision and human-machine collaboration.
    QIU Jing,born in 1977,Ph.D,associate professor.Her main research interests include exoskeleton robot and human factors engineering.
  • Supported by:
    Fundamental Research Funds for the Central Universities(ZYGX2019Z010).

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Abstract: Existing object pose estimation methods cannot provide estimated poses with inter-frame stability.As a result,when the results are directly used in visualization scenarios such as augmented reality,it will cause screen jitter,so it's not suitable enough for application scenarios such as human-machine collaboration.This paper proposes an object pose estimation optimization method that includes multiple methods.By improving the loss function of the original pose estimation method and using causal filtering to optimize the pose estimation result,a stable estimated pose can be obtained.In addition,in order to consummate the eva-luation system of the degree of stability of the pose estimation method,this paper proposes three evaluation indicators:the direct deviation distance DBD,the direction reversal rate DRR and the average displacement angle ADA,which can evaluate the object pose estimation method from multiple viewpoints.Finally,the YCB-STB dataset is used to test,and the method is compared with the original method without optimization.The results show that the proposed method can improve the inter-frame stability of the existing object pose estimation methods without introducing additional resources,and has a small impact on the accuracy of the original method,which satisfies the requirement of object attitude estimation in human-machine collaborative scene.

Key words: Causal filtering, Human-machine collaboration, Loss function, Object pose estimate

CLC Number: 

  • TP242.6
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