  High-accurate localization is crucial for the safety and reliability of
autonomous driving, especially for the information fusion of collective
perception that aims to further improve road safety by sharing information in a
communication network of ConnectedAutonomous Vehicles (CAV). In this scenario,
small localization errors can impose additional difficulty on fusing the
information from different CAVs. In this paper, we propose a RANSAC-based
(RANdom SAmple Consensus) method to correct the relative localization errors
between two CAVs in order to ease the information fusion among the CAVs.
Different from previous LiDAR-based localization algorithms that only take the
static environmental information into consideration, this method also leverages
the dynamic objects for localization thanks to the real-time data sharing
between CAVs. Specifically, in addition to the static objects like poles,
fences, and facades, the object centers of the detected dynamic vehicles are
also used as keypoints for the matching of two point sets. The experiments on
the synthetic dataset COMAP show that the proposed method can greatly decrease
the relative localization error between two CAVs to less than 20cmas far as
there are enough vehicles and poles are correctly detected by bothCAVs.
Besides, our proposed method is also highly efficient in runtime and can be
used in real-time scenarios of autonomous driving.
