Minimizing the object space error for pose estimation: towards the most efficient algorithm
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Authors
Yingwei Han
- School of Economics and Management, Beihang University, Beijing, 100191, China.
Yong Xia
- School of Mathematics and System Sciences, Beihang University, 100191, China.
Ping Li
- School of Economics and Management, Beihang University, Beijing, 100191, China.
Abstract
In this paper, we present an efficient branch-and-bound algorithm to
globally minimize the object space error for the camera pose
estimation. The key idea is to reformulate the pose estimation
model using the optimal Lagrangian multipliers.
Numerical simulation results show that our algorithm usually
terminates in the first iteration and finds an \(\epsilon\)-suboptimal
solution.
Furthermore, the efficiency of our algorithm is demonstrated by a comprehensive numerical
comparison with two well-known heuristics. We also demonstrate the computational
power of our algorithm by comparing it with the state-of-the-art
global optimization package BARON.
Share and Cite
ISRP Style
Yingwei Han, Yong Xia, Ping Li, Minimizing the object space error for pose estimation: towards the most efficient algorithm, Journal of Nonlinear Sciences and Applications, 10 (2017), no. 10, 5540--5551
AMA Style
Han Yingwei, Xia Yong, Li Ping, Minimizing the object space error for pose estimation: towards the most efficient algorithm. J. Nonlinear Sci. Appl. (2017); 10(10):5540--5551
Chicago/Turabian Style
Han, Yingwei, Xia, Yong, Li, Ping. "Minimizing the object space error for pose estimation: towards the most efficient algorithm." Journal of Nonlinear Sciences and Applications, 10, no. 10 (2017): 5540--5551
Keywords
- Pose estimation
- PnP
- robotics
- branch-and-bound
- Lagrangian dual
MSC
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