Globally Optimal Symbolic Hand-Eye Calibration
Hand-eye calibration (HEC) is a kernel technique guaranteeing precision industrial visual servoing and robotic grasping. Extensive studies have been conducted to various closed-form and iterative solutions to HEC problems using different pose parameterizations. However, these approaches are either s...
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| Veröffentlicht in: | IEEE/ASME transactions on mechatronics 2021-06, Vol.26 (3), p.1369-1379 |
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| container_title | IEEE/ASME transactions on mechatronics |
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| creator | Wu, Jin Liu, Ming Zhu, Yilong Zou, Zuhao Dai, Ming-Zhe Zhang, Chengxi Jiang, Yi Li, Chong |
| description | Hand-eye calibration (HEC) is a kernel technique guaranteeing precision industrial visual servoing and robotic grasping. Extensive studies have been conducted to various closed-form and iterative solutions to HEC problems using different pose parameterizations. However, these approaches are either sensitive to input noise or time-consuming for implementation. This article provides a new perspective on a deterministic solution to two major branches of HEC problems of forms \boldsymbol {AX} = \boldsymbol {XB} and \boldsymbol {AX} = \boldsymbol {YB}. We use symbolic methods to derive a globally optimal solution. Different from representatives based on optimization, this method is not only the most accurate against others but also with repeatability of 100%. Experiments via industrial robotic manipulator verify the superiority of the proposed algorithm. |
| doi_str_mv | 10.1109/TMECH.2020.3019306 |
| format | Article |
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Extensive studies have been conducted to various closed-form and iterative solutions to HEC problems using different pose parameterizations. However, these approaches are either sensitive to input noise or time-consuming for implementation. This article provides a new perspective on a deterministic solution to two major branches of HEC problems of forms <inline-formula><tex-math notation="LaTeX">\boldsymbol {AX} = \boldsymbol {XB}</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">\boldsymbol {AX} = \boldsymbol {YB}</tex-math></inline-formula>. We use symbolic methods to derive a globally optimal solution. Different from representatives based on optimization, this method is not only the most accurate against others but also with repeatability of 100%. Experiments via industrial robotic manipulator verify the superiority of the proposed algorithm.]]></description><identifier>ISSN: 1083-4435</identifier><identifier>EISSN: 1941-014X</identifier><identifier>DOI: 10.1109/TMECH.2020.3019306</identifier><identifier>CODEN: IATEFW</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Algorithms ; Calibration ; Global solution ; Grasping (robotics) ; hand-eye calibration (HEC) ; IEEE transactions ; Industrial robots ; Iterative methods ; Iterative solution ; Jacobian matrices ; Noise sensitivity ; Optimization ; Quaternions ; Robot arms ; robotic manipulator ; robotic perception ; Service robots ; symbolic computation ; Visual control</subject><ispartof>IEEE/ASME transactions on mechatronics, 2021-06, Vol.26 (3), p.1369-1379</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c339t-1972a1e550f6dd5ccfffccb620a0276e219494723bdf4c55ac80fcc4ccf427a83</citedby><cites>FETCH-LOGICAL-c339t-1972a1e550f6dd5ccfffccb620a0276e219494723bdf4c55ac80fcc4ccf427a83</cites><orcidid>0000-0002-1742-0963 ; 0000-0002-3130-6497 ; 0000-0002-3399-916X ; 0000-0002-4500-238X ; 0000-0001-5930-4170 ; 0000-0001-8927-0119</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9177352$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9177352$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Wu, Jin</creatorcontrib><creatorcontrib>Liu, Ming</creatorcontrib><creatorcontrib>Zhu, Yilong</creatorcontrib><creatorcontrib>Zou, Zuhao</creatorcontrib><creatorcontrib>Dai, Ming-Zhe</creatorcontrib><creatorcontrib>Zhang, Chengxi</creatorcontrib><creatorcontrib>Jiang, Yi</creatorcontrib><creatorcontrib>Li, Chong</creatorcontrib><title>Globally Optimal Symbolic Hand-Eye Calibration</title><title>IEEE/ASME transactions on mechatronics</title><addtitle>TMECH</addtitle><description><![CDATA[Hand-eye calibration (HEC) is a kernel technique guaranteeing precision industrial visual servoing and robotic grasping. Extensive studies have been conducted to various closed-form and iterative solutions to HEC problems using different pose parameterizations. However, these approaches are either sensitive to input noise or time-consuming for implementation. This article provides a new perspective on a deterministic solution to two major branches of HEC problems of forms <inline-formula><tex-math notation="LaTeX">\boldsymbol {AX} = \boldsymbol {XB}</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">\boldsymbol {AX} = \boldsymbol {YB}</tex-math></inline-formula>. We use symbolic methods to derive a globally optimal solution. Different from representatives based on optimization, this method is not only the most accurate against others but also with repeatability of 100%. Experiments via industrial robotic manipulator verify the superiority of the proposed algorithm.]]></description><subject>Algorithms</subject><subject>Calibration</subject><subject>Global solution</subject><subject>Grasping (robotics)</subject><subject>hand-eye calibration (HEC)</subject><subject>IEEE transactions</subject><subject>Industrial robots</subject><subject>Iterative methods</subject><subject>Iterative solution</subject><subject>Jacobian matrices</subject><subject>Noise sensitivity</subject><subject>Optimization</subject><subject>Quaternions</subject><subject>Robot arms</subject><subject>robotic manipulator</subject><subject>robotic perception</subject><subject>Service robots</subject><subject>symbolic computation</subject><subject>Visual control</subject><issn>1083-4435</issn><issn>1941-014X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kD1PwzAQhi0EEqXwB2CJxJxw54-4HlFUWqSiDhSJzXIcW0rlNsVJh_x7XFox3Q3Pex8PIY8IBSKol83HvFoWFCgUDFAxKK_IBBXHHJB_X6ceZiznnIlbctf3WwDgCDghxSJ0tQlhzNaHod2ZkH2Ou7oLrc2WZt_k89FllQltHc3Qdvt7cuNN6N3DpU7J19t8Uy3z1XrxXr2ucsuYGnJUkhp0QoAvm0ZY6723ti4pGKCydDSdprikrG48t0IYO4ME8ARyKs2MTcnzee4hdj9H1w962x3jPq3UVAgmFHKJiaJnysau76Pz-hDTD3HUCPrkRf950Scv-uIlhZ7OodY59x9QKCUTlP0CEQZdoQ</recordid><startdate>202106</startdate><enddate>202106</enddate><creator>Wu, Jin</creator><creator>Liu, Ming</creator><creator>Zhu, Yilong</creator><creator>Zou, Zuhao</creator><creator>Dai, Ming-Zhe</creator><creator>Zhang, Chengxi</creator><creator>Jiang, Yi</creator><creator>Li, Chong</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0002-1742-0963</orcidid><orcidid>https://orcid.org/0000-0002-3130-6497</orcidid><orcidid>https://orcid.org/0000-0002-3399-916X</orcidid><orcidid>https://orcid.org/0000-0002-4500-238X</orcidid><orcidid>https://orcid.org/0000-0001-5930-4170</orcidid><orcidid>https://orcid.org/0000-0001-8927-0119</orcidid></search><sort><creationdate>202106</creationdate><title>Globally Optimal Symbolic Hand-Eye Calibration</title><author>Wu, Jin ; Liu, Ming ; Zhu, Yilong ; Zou, Zuhao ; Dai, Ming-Zhe ; Zhang, Chengxi ; Jiang, Yi ; Li, Chong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c339t-1972a1e550f6dd5ccfffccb620a0276e219494723bdf4c55ac80fcc4ccf427a83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Algorithms</topic><topic>Calibration</topic><topic>Global solution</topic><topic>Grasping (robotics)</topic><topic>hand-eye calibration (HEC)</topic><topic>IEEE transactions</topic><topic>Industrial robots</topic><topic>Iterative methods</topic><topic>Iterative solution</topic><topic>Jacobian matrices</topic><topic>Noise sensitivity</topic><topic>Optimization</topic><topic>Quaternions</topic><topic>Robot arms</topic><topic>robotic manipulator</topic><topic>robotic perception</topic><topic>Service robots</topic><topic>symbolic computation</topic><topic>Visual control</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wu, Jin</creatorcontrib><creatorcontrib>Liu, Ming</creatorcontrib><creatorcontrib>Zhu, Yilong</creatorcontrib><creatorcontrib>Zou, Zuhao</creatorcontrib><creatorcontrib>Dai, Ming-Zhe</creatorcontrib><creatorcontrib>Zhang, Chengxi</creatorcontrib><creatorcontrib>Jiang, Yi</creatorcontrib><creatorcontrib>Li, Chong</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005–Present</collection><collection>IEEE All-Society Periodicals Package (ASPP) Online</collection><collection>IEEE/IET Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>IEEE/ASME transactions on mechatronics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Wu, Jin</au><au>Liu, Ming</au><au>Zhu, Yilong</au><au>Zou, Zuhao</au><au>Dai, Ming-Zhe</au><au>Zhang, Chengxi</au><au>Jiang, Yi</au><au>Li, Chong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Globally Optimal Symbolic Hand-Eye Calibration</atitle><jtitle>IEEE/ASME transactions on mechatronics</jtitle><stitle>TMECH</stitle><date>2021-06</date><risdate>2021</risdate><volume>26</volume><issue>3</issue><spage>1369</spage><epage>1379</epage><pages>1369-1379</pages><issn>1083-4435</issn><eissn>1941-014X</eissn><coden>IATEFW</coden><abstract><![CDATA[Hand-eye calibration (HEC) is a kernel technique guaranteeing precision industrial visual servoing and robotic grasping. Extensive studies have been conducted to various closed-form and iterative solutions to HEC problems using different pose parameterizations. However, these approaches are either sensitive to input noise or time-consuming for implementation. This article provides a new perspective on a deterministic solution to two major branches of HEC problems of forms <inline-formula><tex-math notation="LaTeX">\boldsymbol {AX} = \boldsymbol {XB}</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">\boldsymbol {AX} = \boldsymbol {YB}</tex-math></inline-formula>. We use symbolic methods to derive a globally optimal solution. Different from representatives based on optimization, this method is not only the most accurate against others but also with repeatability of 100%. Experiments via industrial robotic manipulator verify the superiority of the proposed algorithm.]]></abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TMECH.2020.3019306</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0002-1742-0963</orcidid><orcidid>https://orcid.org/0000-0002-3130-6497</orcidid><orcidid>https://orcid.org/0000-0002-3399-916X</orcidid><orcidid>https://orcid.org/0000-0002-4500-238X</orcidid><orcidid>https://orcid.org/0000-0001-5930-4170</orcidid><orcidid>https://orcid.org/0000-0001-8927-0119</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Calibration Global solution Grasping (robotics) hand-eye calibration (HEC) IEEE transactions Industrial robots Iterative methods Iterative solution Jacobian matrices Noise sensitivity Optimization Quaternions Robot arms robotic manipulator robotic perception Service robots symbolic computation Visual control |
| title | Globally Optimal Symbolic Hand-Eye Calibration |
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