Domain Decomposition Based Preconditioned Solver for Bundle Adjustment

Shrutimoy Das, Siddhant Katyan, Pawan Kumar

Abstract: We propose Domain Decomposed Bundle Adjustment (DDBA), a robust and efficient solver for the bundle adjustment problem. Bundle adjustment (BA) is generally formulated as a non-linear least squares problem and is solved by some variant of the Levenberg-Marquardt (LM) algorithm. Each iteration of the LM algorithm requires solving a system of normal equations, which becomes computationally expensive with the increase in problem size. The coefficient matrix of this system has a sparse structure which can be exploited for simplifying the computations in this step. We propose a technique for approximating the Schur complement of the matrix, and use this approximation to construct a preconditioner, that can be used with the Generalized Minimal Residual (GMRES) algorithm for solving the system of equations. Our experiments on the BAL dataset show that the proposed method for solving the system is faster than GMRES solve preconditioned with block Jacobi and more memory efficient than direct solve.