  Place recognition is a key module for long-term SLAM systems. Current
LiDAR-based place recognition methods usually use representations of point
clouds such as unordered points or range images. These methods achieve high
recall rates of retrieval, but their performance may degrade in the case of
view variation or scene changes. In this work, we explore the potential of a
different representation in place recognition, i.e. bird's eye view (BEV)
images. We observe that the structural contents of BEV images are less
influenced by rotations and translations of point clouds. We validate that,
without any delicate design, a simple VGGNet trained on BEV images achieves
comparable performance with the state-of-the-art place recognition methods in
scenes of slight viewpoint changes. For more robust place recognition, we
design a rotation-invariant network called BEVPlace. We use group convolution
to extract rotation-equivariant local features from the images and NetVLAD for
global feature aggregation. In addition, we observe that the distance between
BEV features is correlated with the geometry distance of point clouds. Based on
the observation, we develop a method to estimate the position of the query
cloud, extending the usage of place recognition. The experiments conducted on
large-scale public datasets show that our method 1) achieves state-of-the-art
performance in terms of recall rates, 2) is robust to view changes, 3) shows
strong generalization ability, and 4) can estimate the positions of query point
clouds. Source codes are publicly available at
https://github.com/zjuluolun/BEVPlace.
