  In this paper, we propose a new image-based visual place recognition (VPR)
framework by exploiting the structural cues in bird's-eye view (BEV) from a
single monocular camera. The motivation arises from two key observations about
place recognition methods based on both appearance and structure: 1) For the
methods relying on LiDAR sensors, the integration of LiDAR in robotic systems
has led to increased expenses, while the alignment of data between different
sensors is also a major challenge. 2) Other image-/camera-based methods,
involving integrating RGB images and their derived variants (eg, pseudo depth
images, pseudo 3D point clouds), exhibit several limitations, such as the
failure to effectively exploit the explicit spatial relationships between
different objects. To tackle the above issues, we design a new BEV-enhanced VPR
framework, namely BEV$^2$PR, generating a composite descriptor with both visual
cues and spatial awareness based on a single camera. The key points lie in: 1)
We use BEV features as an explicit source of structural knowledge in
constructing global features. 2) The lower layers of the pre-trained backbone
from BEV generation are shared for visual and structural streams in VPR,
facilitating the learning of fine-grained local features in the visual stream.
3) The complementary visual and structural features can jointly enhance VPR
performance. Our BEV$^2$PR framework enables consistent performance
improvements over several popular aggregation modules for RGB global features.
The experiments on our collected VPR-NuScenes dataset demonstrate an absolute
gain of 2.47% on Recall@1 for the strong Conv-AP baseline to achieve the best
performance in our setting, and notably, a 18.06% gain on the hard set. The
code and dataset will be available at https://github.com/FudongGe/BEV2PR.
