Robust improvement solution to perspective-n-point problem

Perspective-n-point is a classical computer vision problem that uses three-dimensional points and image pixels to estimate camera pose. The visual robot often loses its position when the camera moves too fast or the environment changes. Perspective-n-point is used to relocate robot position, but the distribution of three-dimensional points in the world frame and different choices of coordinates affect the perspective-n-point performance and make perspective-n-point results less robust and inaccurate. In this study, we review the previous perspective-n-point algorithms and provide their disadvantages when facing three-dimensional points with large variances. According to the drawbacks of previous perspective-n-point methods, we propose a normalization method inspired by the homogeneous matrix calculation process to increase perspective-n-point algorithm accuracy and robustness. The experimental results demonstrate that the proposed perspective-n-point method is robust to different choices of coordinates and is thus better than other state-of-art perspective-n-point methods. Considering that the true camera pose is difficult to obtain, the former perspective-n-point solution validation experiment is mostly based on simulated image data. In this study, we design a new experiment based on total station and chessboard to verify the robustness and accuracy of the perspective-n-point algorithm.