Name
Abstract | ||
|---|---|---|
In this paper, we propose a practical and efficient method for finding the globally optimal solution to the problem of determining the pose of an object. We present a framework that allows us to use point-to-point, point-to-line, and point-to-plane correspondences for solving various types of pose and registration problems involving euclidean (or similarity) transformations. Traditional methods such as the iterative closest point algorithm or bundle adjustment methods for camera pose may get trapped in local minima due to the nonconvexity of the corresponding optimization problem. Our approach of solving the mathematical optimization problems guarantees global optimality. The optimization scheme is based on ideas from global optimization theory, in particular convex underestimators in combination with branch-and-bound methods. We provide a provably optimal algorithm and demonstrate good performance on both synthetic and real data. We also give examples of where traditional methods fail due to the local minima problem. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/TPAMI.2008.131 | IEEE Trans. Pattern Anal. Mach. Intell. |
Keywords | Field | DocType |
mathematical optimization problem,optimal solution,registration problem,iterative closest point algorithm,traditional method,corresponding optimization problem,optimization scheme,local minima problem,global optimality,global optimization theory,euclidean registration problems,branch-and-bound methods,point to point,image reconstruction,robot kinematics,convex programming,bundle adjustment,pose estimation,algorithms,global optimization,similarity transformation,constrained optimization,optimization problem,maximum likelihood estimation,artificial intelligence,image registration,local minima,branch and bound,computational geometry,cost function,minimisation | Branch and bound,Mathematical optimization,Global optimization,Iterative method,Computer science,Maxima and minima,Pose,Convex optimization,Optimization problem,Constrained optimization | Journal |
Volume | Issue | ISSN |
31 | 5 | 0162-8828 |
Citations | PageRank | References |
52 | 1.55 | 13 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Carl Olsson | 1 | 344 | 27.31 |
| Fredrik Kahl | 2 | 1415 | 92.61 |
| Magnus Oskarsson | 3 | 196 | 22.85 |