Atsutake Kosuge, Keisuke Yamamoto, Y. Akamine, T. Yamawaki, T. Oshima
{"title":"A 4.8x Faster FPGA-Based Iterative Closest Point Accelerator for Object Pose Estimation of Picking Robot Applications","authors":"Atsutake Kosuge, Keisuke Yamamoto, Y. Akamine, T. Yamawaki, T. Oshima","doi":"10.1109/FCCM.2019.00072","DOIUrl":null,"url":null,"abstract":"An FPGA-based accelerator for the iterative-closest-point (ICP) algorithm has been proposed, which achieves 4.8-times-faster object-pose estimation by a picking robot compared with the state-of-the-art technique. Experiments of the proposed FPGA-based ICP accelerator using Amazon Picking Contest data sets have confirmed that the object-pose estimation by the ICP takes only 0.6 seconds, and the entire picking process takes 2.0 seconds with power consumption of 6.0 W.","PeriodicalId":116955,"journal":{"name":"2019 IEEE 27th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE 27th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FCCM.2019.00072","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
Abstract
An FPGA-based accelerator for the iterative-closest-point (ICP) algorithm has been proposed, which achieves 4.8-times-faster object-pose estimation by a picking robot compared with the state-of-the-art technique. Experiments of the proposed FPGA-based ICP accelerator using Amazon Picking Contest data sets have confirmed that the object-pose estimation by the ICP takes only 0.6 seconds, and the entire picking process takes 2.0 seconds with power consumption of 6.0 W.