Laser map aided visual inertial localization in changing environment
This addresses the problem of robust localization for autonomous systems in dynamic outdoor settings, though it appears incremental as it builds on existing visual inertial methods with map optimization.
The paper tackles long-term visual localization in changing outdoor environments by proposing a visual inertial localization framework that uses a LiDAR-built map, achieving satisfactory results across different seasons and route directions.
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.