Synthetic Learning Set for Object Pose Estimation: Initial Experiments

被引：0

作者：

Lee, Joo-Haeng ^{[1
,2
]}

Yun, Woo-Han ^{[1
]}

Lee, Jaeyeon ^{[1
]}

Kim, Jaehong ^{[1
]}

机构：

[1] ETRI, Human Machine Interact Grp, Daejeon 34129, South Korea

[2] Univ Sci & Technol, Comp Software Dept, Daejeon 34113, South Korea

来源：

2017 14TH INTERNATIONAL CONFERENCE ON UBIQUITOUS ROBOTS AND AMBIENT INTELLIGENCE (URAI) | 2017年

关键词：

Synthetic learning set; pose estimation; machine learning; robot manipulation;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We summarize a method to generate a synthetic learning set for object pose estimation in robotic manipulation tasks. Exploiting modern computer graphics techniques, our synthetic learning set satisfies the requirements both in quantitative diversity and qualitative precision. We report the partial results of initial experiments and discuss some future research directions.

引用

页码：106 / 108

页数：3

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