125 / 2023-09-20 15:39:57
Adaptive Weighted 6d Pose Estimation Method using RGB-D Data
deep learning,RGB – D,6D pose estimation,robotic grasping
Draft Accepted
Hongxuan Guo / Xi'an Jiaotong University
Haitao Xiao / Xi'an Jiaotong University
Zining Zhao / Xi'an Jiaotong University
Aiming to address the challenge of underutilizing color and depth information when estimating the six-degree-of-freedom (6-DoF) target pose for a single RGB-D image, a deep learning framework based on pixel-wise adaptive feature embedding fusion network is proposed. This method is employed for estimating the 6-DoF poses of a known set of objects in highly cluttered scenes. The network is tested on the publicly available LineMOD dataset, and experimental results demonstrate that, compared to existing methods for 6-DoF pose estimation of similar types, the model presented in this paper achieves superior accuracy in predicting the 6-DoF poses. When evaluated using the same criteria, the average accuracy reaches 96.5%.
Important Date
  • Conference Date

    Nov 02

    2023

    to

    Nov 04

    2023

  • Dec 15 2023

    Draft paper submission deadline

  • Dec 20 2023

    Registration deadline

Sponsored By
IEEE Instrumentation and Measurement Society
Xidian University