Robust Single Rotation Averaging
This addresses rotation averaging in computer vision and robotics, offering incremental improvements in speed and robustness.
The paper tackles the problem of single rotation averaging by proposing a robust method using the Weiszfeld algorithm, resulting in performance equal to state-of-the-art but with 2-4 times faster computation.
We propose a novel method for single rotation averaging using the Weiszfeld algorithm. Our contribution is threefold: First, we propose a robust initialization based on the elementwise median of the input rotation matrices. Our initial solution is more accurate and robust than the commonly used chordal $L_2$-mean. Second, we propose an outlier rejection scheme that can be incorporated in the Weiszfeld algorithm to improve the robustness of $L_1$ rotation averaging. Third, we propose a method for approximating the chordal $L_1$-mean using the Weiszfeld algorithm. An extensive evaluation shows that both our method and the state of the art perform equally well with the proposed outlier rejection scheme, but ours is $2-4$ times faster.