Mi Liu , Jingjing Guo , Lu Deng , Songyue Wang , Huiguang Wang
{"title":"Enhanced vision-based 6-DoF pose estimation for robotic rebar tying","authors":"Mi Liu , Jingjing Guo , Lu Deng , Songyue Wang , Huiguang Wang","doi":"10.1016/j.autcon.2025.105999","DOIUrl":null,"url":null,"abstract":"<div><div>Rebar tying is a labor-intensive and time-consuming task that involves repeatedly securing rebar intersections. While rebar tying robots have been developed to automate this process, most research focuses on tying point localization for horizontal ties, neglecting the 6 degrees of freedom (DoF) tying pose estimation required for reinforcement skeletons with rebar planes in various directions. This paper presents an any-direction robotic rebar tying method (AnyDirTying) for 6-DoF tying pose estimation. First, a deep learning-based keypoint detection algorithm extracts point clouds from rebar intersections. Next, a coarse-to-fine point cloud registration method is developed to improve the accuracy and stability of rebar pose estimation. Finally, a symmetry-aware tying strategy based on the minimum rotation angle is designed to optimize the tying pose and shorten the motion path. The proposed AnyDirTying enables flexible, accurate, and efficient tying pose estimation, expanding the applications of robotic rebar tying and reducing reliance on manual labor.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"171 ","pages":"Article 105999"},"PeriodicalIF":9.6000,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Automation in Construction","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0926580525000391","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Rebar tying is a labor-intensive and time-consuming task that involves repeatedly securing rebar intersections. While rebar tying robots have been developed to automate this process, most research focuses on tying point localization for horizontal ties, neglecting the 6 degrees of freedom (DoF) tying pose estimation required for reinforcement skeletons with rebar planes in various directions. This paper presents an any-direction robotic rebar tying method (AnyDirTying) for 6-DoF tying pose estimation. First, a deep learning-based keypoint detection algorithm extracts point clouds from rebar intersections. Next, a coarse-to-fine point cloud registration method is developed to improve the accuracy and stability of rebar pose estimation. Finally, a symmetry-aware tying strategy based on the minimum rotation angle is designed to optimize the tying pose and shorten the motion path. The proposed AnyDirTying enables flexible, accurate, and efficient tying pose estimation, expanding the applications of robotic rebar tying and reducing reliance on manual labor.
期刊介绍:
Automation in Construction is an international journal that focuses on publishing original research papers related to the use of Information Technologies in various aspects of the construction industry. The journal covers topics such as design, engineering, construction technologies, and the maintenance and management of constructed facilities.
The scope of Automation in Construction is extensive and covers all stages of the construction life cycle. This includes initial planning and design, construction of the facility, operation and maintenance, as well as the eventual dismantling and recycling of buildings and engineering structures.