Towards the Application of Linear Programming Methods For Multi-Camera Pose Estimation

TL;DR

This paper introduces a separation-based optimization algorithm for multi-camera pose estimation that simplifies computations by avoiding matrix inversion, enabling the use of certain nonlinear and quadratic functions for minimizing reprojection error.

Contribution

The paper proposes a novel separation approach that eliminates matrix inversion in multi-camera pose estimation, allowing for more efficient optimization of specific nonlinear and quadratic functions.

Findings

Elimination of matrix inversion in the optimization process

Effective minimization of reprojection error using the proposed method

Potential for improved computational efficiency in pose estimation

Abstract

We presented a separation based optimization algorithm which, rather than optimization the entire variables altogether, This would allow us to employ: 1) a class of nonlinear functions with three variables and 2) a convex quadratic multivariable polynomial, for minimization of reprojection error. Neglecting the inversion required to minimize the nonlinear functions, in this paper we demonstrate how separation allows eradication of matrix inversion.