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Robust registration of point sets using iteratively reweighted least squares
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Mathematical Science.ORCID iD: 0000-0002-7566-3656
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Mathematical Science.
2014 (English)In: Computational optimization and applications, ISSN 0926-6003, E-ISSN 1573-2894, Vol. 58, no 3, p. 543-561Article in journal (Refereed) Published
Abstract [en]

Registration of point sets is done by finding a rotation and translation that produces a best fit between a set of data points and a set of model points. We use robust M-estimation techniques to limit the influence of outliers, more specifically a modified version of the iterative closest point algorithm where we use iteratively re-weighed least squares to incorporate the robustness. We prove convergence with respect to the value of the objective function for this algorithm. A comparison is also done of different criterion functions to figure out their abilities to do appropriate point set fits, when the sets of data points contains outliers. The robust methods prove to be superior to least squares minimization in this setting.

Place, publisher, year, edition, pages
2014. Vol. 58, no 3, p. 543-561
National Category
Computational Mathematics
Research subject
Scientific Computing
Identifiers
URN: urn:nbn:se:ltu:diva-3921DOI: 10.1007/s10589-014-9643-2ISI: 000337084900002Scopus ID: 2-s2.0-84904999267Local ID: 1c497761-d55b-4956-85e6-e160c9668a13OAI: oai:DiVA.org:ltu-3921DiVA, id: diva2:976783
Note
Validerad; 2014; 20140206 (berper)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-07-10Bibliographically approved

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Publisher's full textScopushttp://link.springer.com/article/10.1007%2Fs10589-014-9643-2

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Bergström, PerEdlund, Ove

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