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.

翻译：本文研究了在360度视觉地点识别（VPR）中采用鸟瞰图（BEV）表示的优势。我们提出了一种新颖的网络架构，在特征提取、特征聚合以及视觉-激光雷达融合中运用BEV表示，从而桥接视觉线索与空间感知能力。该方法采用标准卷积网络提取图像特征，并根据预定义的3D网格空间点对特征进行组合。为缓解相机之间的机械和时间错位，我们进一步引入可变形注意力机制来学习补偿量。在BEV特征表示的基础上，采用极坐标变换和离散傅里叶变换进行聚合，该过程被证明具有旋转不变性。此外，图像与点云线索可在同一坐标系中轻松表示，有利于传感器融合实现地点识别。所提出的基于BEV的方法在两种数据集（包含道路场景与非道路场景）上进行了消融实验与对比研究。实验结果验证了假设：与基线方法相比，BEV可通过其优越性能促进VPR。据我们所知，这是在该任务中首次尝试采用BEV表示。