visual inertial localization, is reasonable. But we can still summarize that superior result can be found by specialized globally optimal method.

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\[table.robustness\]

Comparison on visual inertial localization
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Finally, we evaluate all the methods on a real world cross-session visual inertial localization task. The dataset employed is YQ-dataset[@ding2018laser]. In the dataset, there are three sessions collected in summer 2017, denoted as 2017-0823, 2017-0827 and 2017-0828, and one session in winter 2018 after snow denoted as 2018-0129. The 3D map is built with 2017-0823 session and the other three sessions are used to evaluate the localization performance, indicating the changing environment. The details to obtain the 3D-2D point and line correspondences can be found in Appendix. For evaluation, we compute the ground truth relative pose between the query camera and the map by aligning the synchronized LiDAR scans. For the pitch and roll angle, we use the estimation of visual inertial odometry [@mur2017visual].

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\[table.real\]

**Selected cases performance.** We first select several typical examples for evaluation as in [@speciale2017consensus] and the results are shown in Tab. \[table.real\]. The Exp01, Exp02 and Exp03 are cases with pure point features where Exp03 has lines as disturbance and the outlier rate in these 