Robust Single Rotation Averaging Revisited

TL;DR

This paper introduces a robust rotation averaging method that effectively handles up to 99% outliers by minimizing a truncated least deviations cost, improving accuracy over existing techniques.

Contribution

It presents a novel three-step algorithm combining initial solution selection, inlier set estimation, and iterative geodesic L1-mean computation for robust rotation averaging.

Findings

Outperforms current state-of-the-art methods in robustness.

Handles up to 99% outliers with sufficient inliers.

Efficiently computes rotation averages on SO(3).

Abstract

In this work, we propose a novel method for robust single rotation averaging that can efficiently handle an extremely large fraction of outliers. Our approach is to minimize the total truncated least unsquared deviations (TLUD) cost of geodesic distances. The proposed algorithm consists of three steps: First, we consider each input rotation as a potential initial solution and choose the one that yields the least sum of truncated chordal deviations. Next, we obtain the inlier set using the initial solution and compute its chordal $L_2$-mean. Finally, starting from this estimate, we iteratively compute the geodesic $L_1$-mean of the inliers using the Weiszfeld algorithm on $SO(3)$. An extensive evaluation shows that our method is robust against up to 99% outliers given a sufficient number of accurate inliers, outperforming the current state of the art.