Rotation Synchronization

Rotation synchronization aims to recover a set of unknown rotations from noisy measurements of their relative orientations, a crucial problem in various fields like computer vision and robotics. Current research focuses on developing robust algorithms, including primal-dual methods, graph neural networks, and Riemannian subgradient approaches, to address the non-convexity and high dimensionality of the problem, often incorporating techniques to handle noisy or incomplete data. These advancements improve the accuracy and efficiency of rotation estimation, impacting applications such as 3D reconstruction, sensor network localization, and navigation systems. The development of theoretically grounded and empirically validated methods continues to be a central theme.