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

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Author

Summary, in English

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.

Publishing year

2006

Language

English

Pages

1206-1213

Publication/Series

Proceedings - 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006

Volume

1

Document type

Conference paper

Publisher

IEEE - Institute of Electrical and Electronics Engineers Inc.

Topic

  • Mathematics

Keywords

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

Conference name

2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006

Conference date

2006-06-17 - 2006-06-22

Conference place

New York, NY, United States

Status

Published