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题名

Efficient Object Rearrangement via Multi-view Fusion

作者
DOI
发表日期
2024-05-17
ISBN
979-8-3503-8458-1
会议录名称
会议日期
13-17 May 2024
会议地点
Yokohama, Japan
摘要
The prospect of assistive robots aiding in object organization has always been compelling. In an image-goal setting, the robot rearranges the current scene to match the single image captured from the goal scene. The key to an image-goal rearrangement system is estimating the desired placement pose of each object based on the single goal image and observations from the current scene. In order to establish sufficient associations for accurate estimation, the system should observe an object from a viewpoint similar to that in the goal image. Existing image-goal rearrangement systems, due to their reliance on a fixed viewpoint for perception, often require redundant manipulations to randomly adjust an object’s pose for a better perspective. Addressing this inefficiency, we introduce a novel object rearrangement system that employs multi-view fusion. By observing the current scene from multiple viewpoints before manipulating objects, our approach can estimate a more accurate pose without redundant manipulation times. A standard visual localization pipeline at the object level is developed to capitalize on the advantages of multi-view observations. Simulation results demonstrate that the efficiency of our system outperforms existing single-view systems. The effectiveness of our system is further validated in a physical experiment. For videos, please visit https: //sites.google.com/view/multi-view-rearr.
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第一
相关链接[IEEE记录]
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成果类型会议论文
条目标识符http://sustech.caswiz.com/handle/2SGJ60CL/803345
专题工学院_电子与电气工程系
南方科技大学
作者单位
1.Shenzhen Key Laboratory of Robotics and Computer Vision, Southern University of Science and Technology, Shenzhen, China
2.Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China
第一作者单位南方科技大学;  电子与电气工程系
第一作者的第一单位南方科技大学
推荐引用方式
GB/T 7714
Dehao Huang,Chao Tang,Hong Zhang. Efficient Object Rearrangement via Multi-view Fusion[C],2024.
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