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Relocalization is the basis of map-based localization algorithms. Camera andLiDAR map-based methods are pervasive since their robustness under differentscenarios. Generally, mapping and localization using the same sensor havebetter accuracy since matching features between the same type of data iseasier. However, due to the camera's lack of 3D information and the high costof LiDAR, cross-media methods are developing, which combined live image dataand Lidar map. Although matching features between different media ischallenging, we believe cross-media is the tendency for AV relocalization sinceits 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 AVrelocalization tasks. Semantic features are utilized for better interpretationthe correlation between point clouds and image features. What's more,abstracted semantic graph nodes are introduced, and a graph networkarchitecture is integrated to better extract the similarity of semanticfeatures. Validation experiments are conducted on the KITTI odometry dataset.Our results show that CMSG can have comparable or even better accuracy comparedto current single-sensor-based methods at a speed of 25 FPS on NVIDIA 1080 TiGPU.

CMSG Cross-Media Semantic-Graph Feature Matching Algorithm for Autonomous Vehicle Relocalization