Multiple rotation averaging (MRA) is a fundamental optimization problem in 3D vision and robotics that aims to recover globally consistent absolute rotations from noisy relative measurements. Established classical methods, such as L1-IRLS and Shonan, face limitations including local minima susceptibility and reliance on convex relaxations that fail to preserve the exact manifold geometry, leading to reduced accuracy in high-noise scenarios. We introduce IQARS (Iterative Quantum Annealing for Rotation Synchronization), the first algorithm that reformulates MRA as a sequence of local quadratic non-convex sub-problems executable on quantum annealers after binarization, to leverage inherent hardware advantages. IQARS removes convex relaxation dependence and better preserves non-Euclidean rotation manifold geometry while leveraging quantum tunneling and parallelism for efficient solution space exploration. We evaluate IQARS's performance on synthetic and real-world datasets. While current annealers remain in their nascent phase and only support solving problems of limited scale with constrained performance, we observed that IQARS on D-Wave annealers can already achieve ca. 12% higher accuracy than Shonan, i.e., the best-performing classical method evaluated empirically.

翻译：多重旋转平均（MRA）是三维视觉与机器人学中的一个基础优化问题，其目标是从含噪声的相对测量中恢复全局一致性的绝对旋转。现有的经典方法，如L1-IRLS和Shonan，面临诸多局限，包括易陷入局部极小值、依赖无法精确保持流形几何的凸松弛方法，从而导致在高噪声场景下精度下降。我们提出了IQARS（用于旋转同步的迭代量子退火算法），这是首个将MRA重新表述为一系列局部二次非凸子问题的算法，这些子问题在二值化后可在量子退火器上执行，以利用其固有的硬件优势。IQARS消除了对凸松弛的依赖，更好地保持了非欧几里得旋转流形的几何结构，同时利用量子隧穿和并行性进行高效的解空间探索。我们在合成和真实世界数据集上评估了IQARS的性能。尽管当前的退火器仍处于起步阶段，仅支持解决规模有限且性能受限的问题，但我们观察到，在D-Wave退火器上运行的IQARS已经能够实现比Shonan（即经验评估中性能最佳的经典方法）高出约12%的精度。