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 crossmodal data association, the laser map is optimized using multisession laser and visual data to extract the salient and stable subset for visual 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 1 .
To solve this problem, REF first uses LiDAR to build a previous map based on the LiDAR map geometry information, and proposes a hybrid bundle adjustment framework to correct the current pose.
Laser Map Aided Visual Inertial Localization in Changing Environment
2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Computer Science Engineering