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  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.
Olsson et al. REF described a method obtaining a global optimal solution from pointto-point, point-to-line, and point-to-plane correspondences using branch-and-bound.
The Registration Problem Revisited: Optimal Solutions From Points, Lines and Planes
2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)
2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)
Computer Science