  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%).
