angle. The threshold to judge a successful localization is 0.1m for translation error and 0.5 degree for rotation error as in [@miraldo2018minimal]. The result is shown in Fig. \[fig.imu\], indicating that the proposed algorithm can achieve over 90% success rate when the noise increases to 5 degree. This level of noise is far more than the pitch and roll estimations in practice [@bloesch2015robust]. In addition, we can find that the performance is better when employing prioritized progressive search.

![The sensitivity experiment result using proposed algorithm with dimension-wise voting (solid) and prioritized progressive voting (dash).[]{data-label="fig.imu"}](imu3){width="45.00000%"}

Comparison on synthetic datasets
--------------------------------

The comparative methods include the RANSAC-based methods EPnP[@lepetit2009epnp], P3P[@gao2003complete], 2-Entity[@jiao20192] and globally optimal method LMI[@speciale2017consensus]. We use the OpenCV[@itseez2015opencv] implementation of EPnP and P3P. For LMI, we modify their open source code in MATLAB following the paper, since only code for 3D-3D registration is released. In addition, we control the evaluation data having rotation angle less than $60\degree$ and add it as the constraint of LMI, as suggested in [@speciale2017consensus]. The 2-Entity RANSAC is implemented in MATLAB and we select the mixed sampling strategy which utilize both points and lines for pose estimation. All methods are followed by nonlinear refinement on 