A bilinear model based solution to object pose estimation with monocular vision for grasping

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 机器人理论与应用

	A bilinear model based solution to object pose estimation with monocular vision for grasping
	Ou, Zhicai; Liu, Wei; Su, Jianhua
	2011
会议名称	2011 IEEE International Conference on Mechatronics and Automation, ICMA 2011
会议录名称	IEEE International Conference on Mechatronics and Automation ( ICMA)
页码	501-506
会议日期	2011
会议地点	China
摘要	Object grasping is an important step in robotic applications for subsequent operations, such as delivery and assembly. Automatic object pose estimation with monocular vision provides useful visual cues for grasping and makes it flexible. However, some of the pose factors, such as the pitch angle and the yaw angle, are difficult to estimate from the monocular vision. In this paper, a modified bilinear model is used to separate the pitch factor and the yaw factor from the object image so as to estimate the particular pitch angle and yaw angle. The iterative singular vector decomposition (SVD) in bilinear model fitting imposes a great computation burden. Thus, a random projection algorithm is used to reduce the dimension of the data while preserving the performance of the bilinear model. A weighted Euclidian distance based factor identification method, which discriminates the importance of the elements of the factor parameters, is presented to improve the robustness of the factor identification. Furthermore, with the pitch angle and the yaw angle estimated from the modified bilinear model, a three-step object pose estimation solution is proposed. Experiments are performed to verify the proposed pose estimation solution.
关键词	Bilinear Model
收录类别	EI
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/4806
专题	多模态人工智能系统全国重点实验室_机器人理论与应用
通讯作者	Su, Jianhua
作者单位	Institute of Automation, Chinese Academy of Sciences
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Ou, Zhicai,Liu, Wei,Su, Jianhua. A bilinear model based solution to object pose estimation with monocular vision for grasping[C],2011:501-506.

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