A Robust Pose Estimation Method for Robot Grasping in Bin-Picking Scenarios Using Point Cloud

IF 5.2 2区 计算机科学 Q2 ROBOTICS
Yilin Lu, Tingting Wang, Kui Li
{"title":"A Robust Pose Estimation Method for Robot Grasping in Bin-Picking Scenarios Using Point Cloud","authors":"Yilin Lu,&nbsp;Tingting Wang,&nbsp;Kui Li","doi":"10.1002/rob.22571","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>Industrial robot grasping in bin-picking scenarios is challenging. This is mainly due to the need for robots to extract individual parts and find suitable grasping poses accurately and efficiently. This paper addresses this challenge by focusing on the complex morphology of injection-molded corner pieces and proposing a noise-robust pose detection model (NRP-Net) for suction-based grasping. We introduce a directional encoding module to enhance the perception of local structures. We also present an instance segmentation method based on differential features, which we integrate with pose space and visibility attention mechanisms to improve the accuracy of pose estimation. To ensure the correctness of the suction area, we design a sealing detection algorithm suitable for cluttered scenes. Validation in practical scenarios shows an 87.4% success rate in grasping. This demonstrates the effectiveness of our method in bin-picking scenarios and offers a viable solution for industrial robot grasping tasks.</p>\n </div>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"42 7","pages":"3172-3188"},"PeriodicalIF":5.2000,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Field Robotics","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/rob.22571","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ROBOTICS","Score":null,"Total":0}
引用次数: 0

Abstract

Industrial robot grasping in bin-picking scenarios is challenging. This is mainly due to the need for robots to extract individual parts and find suitable grasping poses accurately and efficiently. This paper addresses this challenge by focusing on the complex morphology of injection-molded corner pieces and proposing a noise-robust pose detection model (NRP-Net) for suction-based grasping. We introduce a directional encoding module to enhance the perception of local structures. We also present an instance segmentation method based on differential features, which we integrate with pose space and visibility attention mechanisms to improve the accuracy of pose estimation. To ensure the correctness of the suction area, we design a sealing detection algorithm suitable for cluttered scenes. Validation in practical scenarios shows an 87.4% success rate in grasping. This demonstrates the effectiveness of our method in bin-picking scenarios and offers a viable solution for industrial robot grasping tasks.

Abstract Image

基于点云的机器人抓取姿态鲁棒估计方法
工业机器人在拾取垃圾箱的场景中抓取是具有挑战性的。这主要是由于机器人需要准确有效地提取单个零件并找到合适的抓取姿势。本文通过关注注塑成型角件的复杂形态,并提出了一种用于吸力抓取的噪声鲁棒姿态检测模型(NRP-Net)来解决这一挑战。我们引入了一个定向编码模块来增强局部结构的感知。我们还提出了一种基于差分特征的实例分割方法,将姿态空间和可见性注意机制相结合,提高姿态估计的精度。为了保证吸力区域的正确性,我们设计了一种适用于杂乱场景的密封检测算法。在实际场景中验证,抓取成功率为87.4%。这证明了我们的方法在拾取垃圾桶场景中的有效性,并为工业机器人抓取任务提供了可行的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Journal of Field Robotics
Journal of Field Robotics 工程技术-机器人学
CiteScore
15.00
自引率
3.60%
发文量
80
审稿时长
6 months
期刊介绍: The Journal of Field Robotics seeks to promote scholarly publications dealing with the fundamentals of robotics in unstructured and dynamic environments. The Journal focuses on experimental robotics and encourages publication of work that has both theoretical and practical significance.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信