  Place recognition or loop closure detection is one of the core components in
a full SLAM system. In this paper, aiming at strengthening the relevancy of
local neighboring points and the contextual dependency among global points
simultaneously, we investigate the exploitation of transformer-based network
for feature extraction, and propose a Hierarchical Transformer for Place
Recognition (HiTPR). The HiTPR consists of four major parts: point cell
generation, short-range transformer (SRT), long-range transformer (LRT) and
global descriptor aggregation. Specifically, the point cloud is initially
divided into a sequence of small cells by downsampling and nearest neighbors
searching. In the SRT, we extract the local feature for each point cell. While
in the LRT, we build the global dependency among all of the point cells in the
whole point cloud. Experiments on several standard benchmarks demonstrate the
superiority of the HiTPR in terms of average recall rate, achieving 93.71% at
top 1% and 86.63% at top 1 on the Oxford RobotCar dataset for example.
