feature correspondences containing outliers, RANSAC is the most popular solution employed in many visual navigation system. To deal with the visual localization without inertial measurements, i.e. 6DoF, there have been many variants. In [@gao2003complete][@lepetit2009epnp][@wang2018efficient], point feature correspondences based RANSAC are studied. In [@dhome1989determination][@chen1990pose][@ramalingam2011pose], RANSAC is extended to line features. When inertial measurements are provided, the DoF of the problem is reduced, which is utilized by RANSAC to improve the robustness in [@kneip2011robust][@kukelova2010closed], and extended to both point and line correspondences in [@jiao20192]. As RANSAC is developed on randomized sampling theory, it is simple to implement and has good performance on scenarios with moderate outliers. But its disadvantage is also obvious, including low tolerance against extreme outliers, local convergence and no guarantee of the optimality [@speciale2017consensus].

Outlier resistent estimator
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Another branch to reject outliers is to refer other forms of cost functions instead of the squared error [@mactavish2015all]. In [@zhou2016fast], Geman-McClure cost function is utilized for 3D-3D registration, which is insensitive to outliers. In [@bosse2016robust], M-estimators in several typical robotics problems are presented. Switchable cost function is employed to solve pose graph optimization with outlier loop closures [@sunderhauf2012switchable][@lee2013robust]. A more compact solver for such cost function is dynamic covariance scaling 