outliers (only 2 inliers).'
author:
- |
    Yanmei Jiao$^{1}$, Yue Wang$^{1}$, Bo Fu$^{1}$, Qimeng Tan$^{2}$, Lei Chen$^{2}$, Minhang Wang$^{3}$,\
    Shoudong Huang$^{4}$ and Rong Xiong$^{1}$[^1]
bibliography:
- 'appendix.bib'
title: '**Globally optimal consensus maximization for robust visual inertial localization in point and line map** '
---

Introduction
============

Visual inertial navigation system is popular for state estimation of mobile robots, autonomous vehicles and augmented reality applications. Many efforts have been paid to build accurate, consistent and efficient visual inertial odometry [@li2013high][@leutenegger2015keyframe][@forster2016manifold]. However, its inherent drift is unacceptable in long-term operation, requiring absolute pose estimation for correction. Map based visual inertial localization is therefore an important component in a complete navigation system, of which the goal is to estimate the absolute pose from a set of corresponding 2D image feature points and global 3D map points. In this problem, one main challenge is the robustness of the solver against outliers, i.e. incorrect feature correspondences. When high percentage of correspondences is outlier, the performance of the general pose estimator may be severely degenerated.

Pose estimation with outliers is often stated as a consensus maximization problem. One popular solution is random sample consensus (RANSAC), which has lots of variants [@fischler1981random][@choi1997performance] and has been employed in 