CRPlace: Camera-Radar Fusion with BEV Representation for Place Recognition
CoRR(2024)
Abstract
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
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