  Localization is an essential technique in mobile robotics. In a complex
environment, it is necessary to fuse different localization modules to obtain
more robust results, in which the error model plays a paramount role. However,
exteroceptive sensor-based odometries (ESOs), such as LiDAR/visual odometry,
often deliver results with scene-related error, which is difficult to model
accurately. To address this problem, this research designs a scene-aware error
model for ESO, based on which a multimodal localization fusion framework is
developed. In addition, an end-to-end learning method is proposed to train this
error model using sparse global poses such as GPS/IMU results. The proposed
method is realized for error modeling of LiDAR/visual odometry, and the results
are fused with dead reckoning to examine the performance of vehicle
localization. Experiments are conducted using both simulation and real-world
data of experienced and unexperienced environments, and the experimental
results demonstrate that with the learned scene-aware error models, vehicle
localization accuracy can be largely improved and shows adaptiveness in
unexperienced scenes.
