  Long-term visual localization in outdoor environment is a challenging
problem, especially faced with the cross-seasonal, bi-directional tasks and
changing environment. In this paper we propose a novel visual inertial
localization framework that localizes against the LiDAR-built map. Based on the
geometry information of the laser map, a hybrid bundle adjustment framework is
proposed, which estimates the poses of the cameras with respect to the prior
laser map as well as optimizes the state variables of the online visual
inertial odometry system simultaneously. For more accurate cross-modal data
association, the laser map is optimized using multi-session laser and visual
data to extract the salient and stable subset for localization. To validate the
efficiency of the proposed method, we collect data in south part of our campus
in different seasons, along the same and opposite-direction route. In all
sessions of localization data, our proposed method gives satisfactory results,
and shows the superiority of the hybrid bundle adjustment and map optimization.
