TY - GEN
T1 - A RGB-D based 6D Object Pose Estimation and Its Application in Robotic Grasping
AU - Yu, Sheng
AU - Zhai, Di Hua
AU - Wu, Haoran
AU - Liao, Jun
AU - Xia, Yuanqing
N1 - Publisher Copyright:
© 2021 IEEE
PY - 2021
Y1 - 2021
N2 - Pose estimation of objects is critical to robotic grasping. Local optimization approach has been widely used to minimize the distance of the point pairs to estimate the 6D pose, which, however, is time-consuming and low-accuracy. To conquer this problem, a novel and time-efficient 6D object pose estimation neural network, PoseNet, is proposed in this paper. The input of PoseNet is the RGB-D image and a novel fusion network with channel attention mechanism is used to extract data. The random-sample-consensus-based voting method and rotation anchors are developed to predict, respectively, the translation of object and the rotation of object. The performance evaluation on the YCB-Video dataset show that the real-time inference and high accuracy are guaranteed. The proposed method is also demonstrated by a practical robotic grasping system, where the experiment video is avaliable at https://www.bilibili.com/video/BV1qf4y1s7in.
AB - Pose estimation of objects is critical to robotic grasping. Local optimization approach has been widely used to minimize the distance of the point pairs to estimate the 6D pose, which, however, is time-consuming and low-accuracy. To conquer this problem, a novel and time-efficient 6D object pose estimation neural network, PoseNet, is proposed in this paper. The input of PoseNet is the RGB-D image and a novel fusion network with channel attention mechanism is used to extract data. The random-sample-consensus-based voting method and rotation anchors are developed to predict, respectively, the translation of object and the rotation of object. The performance evaluation on the YCB-Video dataset show that the real-time inference and high accuracy are guaranteed. The proposed method is also demonstrated by a practical robotic grasping system, where the experiment video is avaliable at https://www.bilibili.com/video/BV1qf4y1s7in.
KW - Channel Attention
KW - Instance Segmentation
KW - Pose Estimation
KW - Robotic Grasping
KW - Rotation Anchors
UR - http://www.scopus.com/inward/record.url?scp=85128083066&partnerID=8YFLogxK
U2 - 10.1109/CAC53003.2021.9727342
DO - 10.1109/CAC53003.2021.9727342
M3 - Conference contribution
AN - SCOPUS:85128083066
T3 - Proceeding - 2021 China Automation Congress, CAC 2021
SP - 5953
EP - 5958
BT - Proceeding - 2021 China Automation Congress, CAC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 China Automation Congress, CAC 2021
Y2 - 22 October 2021 through 24 October 2021
ER -