COMPARISON OF NEURAL NETWORK-BASED POSE ESTIMATION APPROACHES FOR MOBILE MANIPULATION

被引:0
作者
Chowdhury, Arindam B. [1 ]
Li, Juncheng [1 ]
Cappelleri, David J. [1 ]
机构
[1] Purdue Univ, Sch Mech Engn, W Lafayette, IN 47907 USA
来源
PROCEEDINGS OF ASME 2021 INTERNATIONAL DESIGN ENGINEERING TECHNICAL CONFERENCES AND COMPUTERS AND INFORMATION IN ENGINEERING CONFERENCE, IDETC-CIE2021, VOL 8A | 2021年
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D O I
暂无
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
In this paper, we present two distinct neural network-based pose estimation approaches for mobile manipulation in factory environments. Synthetic datasets, unique to the factory setting, are created for neural network training in each approach. Approach I uses a CNN in conjunction with RBG and depth images. Approach II uses the DOPE network along with RGB images, CAD dimensions of the objects of interest, and the PnP algorithm. Each approach is evaluated and compared across pipeline complexity, dataset preparation resources, robustness, platform and run-time resources, and pose accuracy for manipulation planning. Finally, recommendations for when to use each method are provided.
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页数:11
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