Optimal non-iterative pose estimation via convex relaxation

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• 作者：Hatem Hmam ; Jijoong Kim
• 关键词：Pose estimation ; PnP ; Robotics ; Semidefinite programming ; Sum-of-squares programming
• 刊名：Image and Vision Computing
• 出版年：2010
• 出版时间：November 2010
• 年：2010
• 卷：28
• 期：11
• 页码：1515-1523
• 全文大小：528 K

文摘

In this paper we present a convex relaxation method that globally solves for the camera position and orientation given a set of image pixel measurements associated with a scene of reference points of known three-dimensional positions. The approach formulates the pose optimization problem as a semidefinite positive relaxation (SDR) program. A comprehensive comparative performance analysis, carried out in the computer simulations section, demonstrates the superior performance of the relaxation method over existing approaches. The computational complexity of the method is O(n), where n is the number of reference points, and is applicable to both coplanar and non-coplanar reference point configurations. The average run-time recorded is 50 ms for an average point count of 100.