  Although significant progress has been made, achieving place recognition in
environments with perspective changes, seasonal variations, and scene
transformations remains challenging. Relying solely on perception information
from a single sensor is insufficient to address these issues. Recognizing the
complementarity between cameras and LiDAR, multi-modal fusion methods have
attracted attention. To address the information waste in existing multi-modal
fusion works, this paper introduces a novel three-channel place descriptor,
which consists of a cascade of image, point cloud, and fusion branches.
Specifically, the fusion-based branch employs a dual-stage pipeline, leveraging
the correlation between the two modalities with latent contacts, thereby
facilitating information interaction and fusion. Extensive experiments on the
KITTI, NCLT, USVInland, and the campus dataset demonstrate that the proposed
place descriptor stands as the state-of-the-art approach, confirming its
robustness and generality in challenging scenarios.
