Shonan Rotation Averaging: Global Optimality by Surfing SO(p)^n

Frank Dellaert

Abstract: Shonan Rotation Averaging is a fast, simple, and elegant ro- tation averaging algorithm that is guaranteed to recover globally optimal solutions under mild assumptions on the measurement noise. Our method employs semidefinite relaxation in order to recover provably globally op- timal solutions of the rotation averaging problem. In contrast to prior work, we show how to solve large-scale instances of these relaxations us- ing manifold minimization on (only slightly) higher-dimensional rotation manifolds, re-using existing high-performance (but local) structure-from- motion pipelines. Our method thus preserves the speed and scalability of current SFM methods, while recovering globally optimal solutions.