  Simultaneous localization and mapping (SLAM) has been a hot research field in
the past years. Against the backdrop of more affordable 3D LiDAR sensors,
research on 3D LiDAR SLAM is becoming increasingly popular. Furthermore, the
re-localization problem with a point cloud map is the foundation for other SLAM
applications. In this paper, a template matching framework is proposed to
re-localize a robot globally in a 3D LiDAR map. This presents two main
challenges. First, most global descriptors for point cloud can only be used for
place detection under a small local area. Therefore, in order to re-localize
globally in the map, point clouds and descriptors(templates) are densely
collected using a reconstructed mesh model at an offline stage by a physical
simulation engine to expand the functional distance of point cloud descriptors.
Second, the increased number of collected templates makes the matching stage
too slow to meet the real-time requirement, for which a cascade matching method
is presented for better efficiency. In the experiments, the proposed framework
achieves 0.2-meter accuracy at about 10Hz matching speed using pure python
implementation with 100k templates, which is effective and efficient for SLAM
applications.
