show the error of estimated rotation giving the most inliers in RANSAC, of which the performance is much worse, indicating inconsistency between the identified inliers and the real inliers. In following experiments, heuristics are applied with BnB as default setting. **Translation voting.** We then compare the voting strategies introduced in Section \[et\]. Now we can evaluate the final accuracy after nonlinear refinement. In addition to efficiency and accuracy, we also evaluate the consistency between the estimated consensus set and the real inlier set (CCI) using precision and recall. As shown in Fig. \[fig.time\], the computation of the prioritized progressive voting is slightly higher than the dimension-wise voting. More importantly, the increased time keeps almost consistent w.r.t outlier rate and correspondences number, which might be explained as no complexity growth for prioritized progressive voting. The CCI and accuracy are shown in the right columns in Tab. \[table.robustness\]. We see that all variants achieve perfect CCI, naturally leading to high accuracy. **Sensitivity to noisy inertial measurements.** As inertial measurements are noisy, it is necessary to evaluate the sensitivity of the proposed method. We add Gaussian noise with zero mean and increasing standard deviation up to 5 degree on both pitch and roll 