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.

翻译：我们提出动力束调整（Power Bundle Adjustment），作为一种扩展型算法用于解决大规模束调整问题。该方法基于逆舒尔补（inverse Schur complement）的幂级数展开，构成了一类新的求解器族，我们称之为逆展开方法。我们从理论上论证了幂级数使用的合理性，并证明了该方法的收敛性。通过使用真实世界的BAL数据集，我们表明所提出的求解器对当前最先进的迭代方法提出了挑战，并显著加速了法方程的求解过程，即使在达到极高精度的条件下也是如此。这种易于实现的求解器还可用于补充近期提出的分布式束调整框架。我们证明，将所提出的动力束调整作为子问题求解器，能够显著提升分布式优化的速度与精度。