The integration of complementary characteristics from camera and radar data has emerged as an effective approach in 3D object detection. However, such fusion-based methods remain unexplored for place recognition, an equally important task for autonomous systems. Given that place recognition relies on the similarity between a query scene and the corresponding candidate scene, the stationary background of a scene is expected to play a crucial role in the task. As such, current well-designed camera-radar fusion methods for 3D object detection can hardly take effect in place recognition because they mainly focus on dynamic foreground objects. In this paper, a background-attentive camera-radar fusion-based method, named CRPlace, is proposed to generate background-attentive global descriptors from multi-view images and radar point clouds for accurate place recognition. To extract stationary background features effectively, we design an adaptive module that generates the background-attentive mask by utilizing the camera BEV feature and radar dynamic points. With the guidance of a background mask, we devise a bidirectional cross-attention-based spatial fusion strategy to facilitate comprehensive spatial interaction between the background information of the camera BEV feature and the radar BEV feature. As the first camera-radar fusion-based place recognition network, CRPlace has been evaluated thoroughly on the nuScenes dataset. The results show that our algorithm outperforms a variety of baseline methods across a comprehensive set of metrics (recall@1 reaches 91.2%).

翻译：[translated abstract in Chinese] 相机与雷达数据的互补特性融合已成为三维目标检测中的有效方法。然而，这类基于融合的方法在同样重要的自主系统地点识别任务中仍未得到探索。由于地点识别依赖查询场景与对应候选场景之间的相似性，场景中的静态背景在该任务中应发挥关键作用。因此，当前专为三维目标检测设计的相机-雷达融合方法难以有效应用于地点识别，因为它们主要关注动态前景物体。本文提出一种关注背景的相机-雷达融合方法CRPlace，通过多视角图像和雷达点云生成关注背景的全局描述子以实现精确地点识别。为有效提取静态背景特征，我们设计了一个自适应模块，利用相机BEV特征和雷达动态点生成背景关注掩码。在背景掩码引导下，我们提出一种基于双向交叉注意力的空间融合策略，以促进相机BEV特征与雷达BEV特征中背景信息之间的全面空间交互。作为首个基于相机-雷达融合的地点识别网络，CRPlace在nuScenes数据集上进行了全面评估。结果表明，我们的算法在综合指标集上优于多种基线方法（recall@1达到91.2%）。