Abstract
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.
Abstract (translated)
本论文研究了在360度视觉位置识别(VPR)中使用鸟眼视角(BEV)表示的优势。我们提出了一种新的网络架构，该架构利用BEV表示在特征提取、特征聚合和视觉-激光雷达融合方面，从而将视觉提示和空间意识连接起来。我们的方法使用标准卷积神经网络提取图像特征，并按照预先定义的三维网格空间点进行特征组合。为了减轻相机之间的机械和时间不一致性，我们引入了可变形的注意力来学习补偿。在BEV特征表示的基础上，我们采用极化变换和离散傅里叶变换进行聚合，表明它是旋转不变的。此外，图像和点云提示可以在相同的坐标系中轻松地陈述，从而提高了位置识别传感器融合的性能。我们提出的基于BEV的方法在两个数据集上进行了 ablation和比较研究，包括在路上和在路上的场景。实验结果验证了我们的假设，即BEV可以通过比基准方法更好的表现来受益于VPR。据我们所知，这是使用BEV表示在这项工作中的第一个尝试。
URL
https://arxiv.org/abs/2305.13814
