{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T17:39:11Z","timestamp":1784569151097,"version":"3.55.0"},"reference-count":27,"publisher":"Emerald","issue":"6","license":[{"start":{"date-parts":[[2022,5,17]],"date-time":"2022-05-17T00:00:00Z","timestamp":1652745600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IR"],"published-print":{"date-parts":[[2022,9,20]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>Many metal workpieces have the characteristics of less texture, symmetry and reflectivity, which presents a challenge to existing pose estimation methods. The purpose of this paper is to propose a pose estimation method for grasping metal workpieces by industrial robots.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>Dual-hypothesis robust point matching registration network (RPM-Net) is proposed to estimate pose from point cloud. The proposed method uses the Point Cloud Library (PCL) to segment workpiece point cloud from scenes and a trained-well robust point matching registration network to estimate pose through dual-hypothesis point cloud registration.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>In the experiment section, an experimental platform is built, which contains a six-axis industrial robot, a binocular structured-light sensor. A data set that contains three subsets is set up on the experimental platform. After training with the emulation data set, the dual-hypothesis RPM-Net is tested on the experimental data set, and the success rates of the three real data sets are 94.0%, 92.0% and 96.0%, respectively.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>The contributions are as follows: first, dual-hypothesis RPM-Net is proposed which can realize the pose estimation of discrete and less-textured metal workpieces from point cloud, and second, a method of making training data sets is proposed using only CAD models with the visualization algorithm of the PCL.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/ir-03-2022-0081","type":"journal-article","created":{"date-parts":[[2022,5,14]],"date-time":"2022-05-14T04:30:14Z","timestamp":1652502614000},"page":"1178-1189","source":"Crossref","is-referenced-by-count":9,"title":["Pose estimation of metal workpieces based on RPM-Net for robot grasping from point cloud"],"prefix":"10.1108","volume":"49","author":[{"given":"Lin","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xi","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tie","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"140","published-online":{"date-parts":[[2022,5,17]]},"reference":[{"key":"key2022091911544413200_ref001","doi-asserted-by":"publisher","first-page":"7156","DOI":"10.1109\/CVPR.2019.00733","article-title":"Pointnetlk: robust & efficient point cloud registration using pointnet","volume-title":"Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition","year":"2019"},{"key":"key2022091911544413200_ref002","doi-asserted-by":"publisher","DOI":"10.1117\/12.57955","article-title":"Method for registration of 3-D shapes","volume-title":"Proc.SPIE","year":"1992"},{"issue":"2","key":"key2022091911544413200_ref003","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1109\/TRO.2013.2289018","article-title":"Data-driven grasp synthesis-A survey","volume":"30","year":"2014","journal-title":"IEEE Transactions on Robotics"},{"key":"key2022091911544413200_ref004","doi-asserted-by":"publisher","first-page":"2080","DOI":"10.1109\/ICRA.2013.6630856","article-title":"Pose estimation using local structure-specific shape and appearance context","volume-title":"Proceedings \u2013 IEEE International Conference on Robotics and Automation","year":"2013"},{"key":"key2022091911544413200_ref005","doi-asserted-by":"publisher","first-page":"210640","DOI":"10.1109\/ACCESS.2020.3034386","article-title":"Detecting 6D poses of target objects from cluttered scenes by learning to align the point cloud patches with the CAD models","volume":"8","year":"2020","journal-title":"IEEE Access"},{"key":"key2022091911544413200_ref006","unstructured":"Do, T.-T., Cai, M., Pham, T. and Reid, I. 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