  Relocalization is the basis of map-based localization algorithms. Camera and
LiDAR map-based methods are pervasive since their robustness under different
scenarios. Generally, mapping and localization using the same sensor have
better accuracy since matching features between the same type of data is
easier. However, due to the camera's lack of 3D information and the high cost
of LiDAR, cross-media methods are developing, which combined live image data
and Lidar map. Although matching features between different media is
challenging, we believe cross-media is the tendency for AV relocalization since
its low cost and accuracy can be comparable to the same-sensor-based methods.
In this paper, we propose CMSG, a novel cross-media algorithm for AV
relocalization tasks. Semantic features are utilized for better interpretation
the correlation between point clouds and image features. What's more,
abstracted semantic graph nodes are introduced, and a graph network
architecture is integrated to better extract the similarity of semantic
features. Validation experiments are conducted on the KITTI odometry dataset.
Our results show that CMSG can have comparable or even better accuracy compared
to current single-sensor-based methods at a speed of 25 FPS on NVIDIA 1080 Ti
GPU.
