A Robust and Fast Method to the Perspective-<i>n</i>-Point Problem for Camera Pose Estimation
Shengbin Zhuang, Zongmin Zhao, Lin Cao, Dongfeng Wang, Chong Fu, Kangning Du
Abstract
This article investigates the perspective <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${n}$ </tex-math></inline-formula> -point (PnP) problem and provides an effective, dependable, and fast optimal solution. The challenging pose estimation problem is changed into the ideal circumstance for solving the transition matrix by estimating the position and orientation of the camera based on known reference points. This method meets both the geometric optimality and the statistical optimal solution by accounting for observation and propagation uncertainty in the solution process. In addition, to further optimize the error caused by the mapping process, this article introduces the Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) algorithm for optimal processing. It saves time and cost because it can iterate the optimal transition matrix and systematic error synchronously. In this article, the algorithm simulation tests are carried out in the standard case, the quasi-singular case, and the planar case. The results of the experiments demonstrate that the strategy suggested in this article can successfully address the issue of camera position estimation. Compared with the existing advanced technology, the accuracy is improved by about 8%–13%, and the time cost is low.