In order to further advance the accuracy and robustness of the incremental parameter estimation-based rotation averaging methods, in this paper, a new member of the Incremental Rotation Averaging (IRA) family is introduced, which is termed as IRAv4. As the most significant feature of the IRAv4, a task-specific connected dominating set is extracted to serve as a more reliable and accurate reference for rotation global alignment. In addition, to further address the limitations of the existing rotation averaging benchmark of relying on the slightly outdated Bundler camera calibration results as ground truths and focusing solely on rotation estimation accuracy, this paper presents a new COLMAP-based rotation averaging benchmark that incorporates a cross check between COLMAP and Bundler, and employ the accuracy of both rotation and downstream location estimation as evaluation metrics, which is desired to provide a more reliable and comprehensive evaluation tool for the rotation averaging research. Comprehensive comparisons between the proposed IRAv4 and other mainstream rotation averaging methods on this new benchmark demonstrate the effectiveness of our proposed approach.

翻译：为进一步提升基于增量参数估计的旋转平均方法的精度与鲁棒性，本文引入增量式旋转平均（IRA）家族新成员，命名为IRAv4。作为IRAv4最显著的特点，我们提取了一个任务特定的连通支配集，作为更可靠、更精确的旋转全局对齐参考。此外，针对现有旋转平均基准过度依赖略显陈旧的Bundler相机标定结果作为真值，且仅关注旋转估计精度的局限性，本文提出了一种基于COLMAP的旋转平均新基准。该基准融入了COLMAP与Bundler的交叉验证，采用旋转估计精度与下游位置估计精度作为评价指标，有望为旋转平均研究提供更可靠、更全面的评估工具。通过在新基准上将所提出的IRAv4与其他主流旋转平均方法进行全面比较，验证了我们方法的有效性。