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Estimating 3-D location parameters using dual number quaternions

dc.contributor.authorWalker, Michael W.en_US
dc.contributor.authorShao, Lejunen_US
dc.contributor.authorVolz, Richard A.en_US
dc.date.accessioned2006-04-10T14:32:17Z
dc.date.available2006-04-10T14:32:17Z
dc.date.issued1991-11en_US
dc.identifier.citationWalker, Michael W., Shao, Lejun, Volz, Richard A. (1991/11)."Estimating 3-D location parameters using dual number quaternions." CVGIP: Image Understanding 54(3): 358-367. <http://hdl.handle.net/2027.42/29059>en_US
dc.identifier.urihttp://www.sciencedirect.com/science/article/B6WDD-4DX436M-32/2/202513c80c88ab2d11dc3e7db7b3e6ffen_US
dc.identifier.urihttps://hdl.handle.net/2027.42/29059
dc.description.abstractThis paper describes a new algorithm for estimating the position and orientation of objects. The problem is formulated as an optimization problem using dual number quaternions. The advantage of using this representation is that the method solves for the location estimate by minimizing a single cost function associated with the sum of the orientation and position errors and thus is expected to have a better performance on the estimation, both in accuracy and in speed. Several forms of sensory information can be used by the algorithm. That is, the measured data can be a combination of measured points on an object's surfaces and measured unit direction vectors located on the object. Simulations have been carried out on a Compaq 386/20 computer and the simulation results are analyzed.en_US
dc.format.extent888659 bytes
dc.format.extent3118 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.language.isoen_US
dc.publisherElsevieren_US
dc.titleEstimating 3-D location parameters using dual number quaternionsen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelComputer Scienceen_US
dc.subject.hlbtoplevelEngineeringen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumArtificial Intelligence Laboratory, EECS Department, University of Michigan, Ann Arbor, Michigan 48109, USAen_US
dc.contributor.affiliationumArtificial Intelligence Laboratory, EECS Department, University of Michigan, Ann Arbor, Michigan 48109, USAen_US
dc.contributor.affiliationotherDepartment of Computer Science, Texas A&M University, College Station, Texas 77843, USAen_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/29059/1/0000092.pdfen_US
dc.identifier.doihttp://dx.doi.org/10.1016/1049-9660(91)90036-Oen_US
dc.identifier.sourceCVGIP: Image Understandingen_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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