  In the field of large-scale SLAM for autonomous driving and mobile robotics,
3D point cloud based place recognition has aroused significant research
interest due to its robustness to changing environments with drastic daytime
and weather variance. However, it is time-consuming and effort-costly to obtain
high-quality point cloud data for place recognition model training and ground
truth for registration in the real world. To this end, a novel
registration-aided 3D domain adaptation network for point cloud based place
recognition is proposed. A structure-aware registration network is introduced
to help to learn features with geometric information and a 6-DoFs pose between
two point clouds with partial overlap can be estimated. The model is trained
through a synthetic virtual LiDAR dataset through GTA-V with diverse weather
and daytime conditions and domain adaptation is implemented to the real-world
domain by aligning the global features. Our results outperform state-of-the-art
3D place recognition baselines or achieve comparable on the real-world Oxford
RobotCar dataset with the visualization of registration on the virtual dataset.
