Rotation Averaging

Rotation averaging aims to estimate a set of absolute rotations from noisy measurements of their relative orientations, a fundamental problem in computer vision and robotics with applications in 3D reconstruction and simultaneous localization and mapping (SLAM). Current research focuses on developing robust algorithms that handle outliers and uncertainties in the input data, employing techniques such as deep matrix factorization, primal-dual methods, spectral relaxations, and hierarchical approaches to improve accuracy and efficiency. These advancements are crucial for improving the reliability and scalability of 3D scene understanding and robotic navigation systems.