  We introduce Power Bundle Adjustment as an expansion type algorithm for
solving large-scale bundle adjustment problems. It is based on the power series
expansion of the inverse Schur complement and constitutes a new family of
solvers that we call inverse expansion methods. We theoretically justify the
use of power series and we prove the convergence of our approach. Using the
real-world BAL dataset we show that the proposed solver challenges the
state-of-the-art iterative methods and significantly accelerates the solution
of the normal equation, even for reaching a very high accuracy. This
easy-to-implement solver can also complement a recently presented distributed
bundle adjustment framework. We demonstrate that employing the proposed Power
Bundle Adjustment as a sub-problem solver significantly improves speed and
accuracy of the distributed optimization.
