with synthetic data (ordinary 3D case, $50\%$ of outliers) to demonstrate the iteration process of R1PP$n$P when the control point $o$ is an outlier. Randomly colored lines are results with different control points. (a) The changes of estimated camera pose between frames $k$ and $k-1$ are complex during the iteration process, based on which it is difficult to decide when to stop the process. (b) It is more robust and efficient to stop the process when no more inliers can be detected.[]{data-label="fig_testTerminationA"}](figures/testTerminationA2.pdf "fig:"){width=".24\textwidth"}

A good *termination condition A* should be able to stop the iteration process as early as possible when point $o$ is an outlier, and do not interrupt when point $o$ is an inlier. We proposed the *termination condition A* with a window size = 20 iterations to seek balance between speed and robustness. This termination condition is not based on the comparison of parameters of adjacent iterations because in R1PP$n$P, the dynamically updated weights $w_i$ may make the convergence process complex, especially when point $o$ is an outlier. As shown in Fig. \[fig\_testTerminationA\](a), with an outlier as the control point, $\left\| {{{\bf{R}}^{(k)}} - {{\bf{R}}^{(k - 1)}}} \right\|$ may take many iterations to converge to zero, which is 