This paper investigates the advantages of using Bird's Eye View (BEV)
representation in 360-degree visual place recognition (VPR). We propose a novel
network architecture that utilizes the BEV representation in feature
extraction, feature aggregation, and vision-LiDAR fusion, which bridges visual
cues and spatial awareness. Our method extracts image features using standard
convolutional networks and combines the features according to pre-defined 3D
grid spatial points. To alleviate the mechanical and time misalignments between
cameras, we further introduce deformable attention to learn the compensation.
Upon the BEV feature representation, we then employ the polar transform and the
Discrete Fourier transform for aggregation, which is shown to be
rotation-invariant. In addition, the image and point cloud cues can be easily
stated in the same coordinates, which benefits sensor fusion for place
recognition. The proposed BEV-based method is evaluated in ablation and
comparative studies on two datasets, including on-the-road and off-the-road
scenarios. The experimental results verify the hypothesis that BEV can benefit
VPR by its superior performance compared to baseline methods. To the best of
our knowledge, this is the first trial of employing BEV representation in this
task.

本文探讨了采用鸟瞰图在 360 度视觉地点识别（VPR）中的优势，提出了一种利用 BEV 表示的新型网络架构，该架构将视觉线索和空间感知进行桥接，并在两个数据集上进行了验证。