Grasp detection via visual rotation object detection and point cloud spatial feature scoring

doi:10.1177/17298814211055577

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 脑机融合与认知评估

	Grasp detection via visual rotation object detection and point cloud spatial feature scoring
	Wang, Jie1,2 ; Li, Shuxiao1,2
发表期刊	INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS
ISSN	1729-8814
	2021-11-01
卷号	18 期号:6 页码:15
通讯作者	Li, Shuxiao(shuxiao.li@ia.ac.cn)
摘要	Accurately detecting the appropriate grasp configurations is the central task for the robot to grasp an object. Existing grasp detection methods usually overlook the depth image or only regard it as a two-dimensional distance image, which makes it difficult to capture the three-dimensional structural characteristics of target object. In this article, we transform the depth image to point cloud and propose a two-stage grasp detection method based on candidate grasp detection from RGB image and spatial feature rescoring from point cloud. Specifically, we first adopt the recently proposed high-performance rotation object detection method for aerial images, named R3Det, to grasp detection task, obtaining the candidate grasp boxes and their appearance scores. Then, point clouds within each candidate grasp box are normalized and evaluated to get the point cloud quality scores, which are fused with the established point cloud quantity scoring model to obtain spatial scores. Finally, appearance scores and their corresponding spatial scores are combined to output high-quality grasp detection results. The proposed method effectively fuses three types of grasp scoring modules, thus is called Score Fusion Grasp Net. Besides, we propose and adopt top-k grasp metric to effectively reflect the success rate of algorithm in actual grasp execution. Score Fusion Grasp Net obtains 98.5% image-wise accuracy and 98.1% object-wise accuracy on Cornell Grasp Dataset, which exceeds the performances of state-of-the-art methods. We also use the robotic arm to conduct physical grasp experiments on 15 kinds of household objects and 11 kinds of adversarial objects. The results show that the proposed method still has a high success rate when facing new objects.
关键词	Robotic grasp detection point cloud object detection deep learning
DOI	10.1177/17298814211055577
关键词[WOS]	LESSONS
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[U19B2033] ; National Natural Science Foundation of China[62076020]
项目资助者	National Natural Science Foundation of China
WOS研究方向	Robotics
WOS类目	Robotics
WOS记录号	WOS:000720135100001
出版者	SAGE PUBLICATIONS INC
七大方向——子方向分类	机器人感知与决策
引用统计	被引频次：4[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/46471
专题	多模态人工智能系统全国重点实验室_脑机融合与认知评估
通讯作者	Li, Shuxiao
作者单位	1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
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	Wang, Jie,Li, Shuxiao. Grasp detection via visual rotation object detection and point cloud spatial feature scoring[J]. INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS,2021,18(6):15.
APA	Wang, Jie,&Li, Shuxiao.(2021).Grasp detection via visual rotation object detection and point cloud spatial feature scoring.INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS,18(6),15.
MLA	Wang, Jie,et al."Grasp detection via visual rotation object detection and point cloud spatial feature scoring".INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 18.6(2021):15.