The registration problem revisited: Optimal solutions from points, lines and planes

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceedingpeer-review

Abstract

In this paper we propose a practical and efficient method for finding the globally optimal solution to the problem of pose estimation of a known object. We present a framework that allows us to use both point-to-point, point-to-line and point-to-plane correspondences in the optimization algorithm. Traditional methods such as the iterative closest point algorithm may get trapped in local minima due to the non-convexity of the problem, however, our approach guarantees global optimality. The approach is based on ideas from global optimization theory, in particular, convex under-estimators in combination with branch and bound. We provide a provably optimal algorithm and demonstrate good performance on both synthetic and real data.
Original languageEnglish
Title of host publicationProceedings - 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006
PublisherIEEE - Institute of Electrical and Electronics Engineers Inc.
Pages1206-1213
Volume1
DOIs
Publication statusPublished - 2006
Event2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006 - New York, NY, United States
Duration: 2006 Jun 172006 Jun 22

Publication series

Name
Volume1

Conference

Conference2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006
Country/TerritoryUnited States
CityNew York, NY
Period2006/06/172006/06/22

Subject classification (UKÄ)

  • Mathematics

Free keywords

  • Under-estimators
  • Non-convexity
  • Point-to-line
  • Point-to-plane

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