Camera-LiDAR Fusion with Latent Contact for Place Recognition in Challenging Cross-Scenes
This work addresses place recognition for autonomous systems in challenging cross-scenes, representing an incremental improvement over existing multi-modal fusion methods.
The paper tackles place recognition in environments with perspective changes, seasonal variations, and scene transformations by introducing a novel three-channel place descriptor that fuses camera and LiDAR data, achieving state-of-the-art results on multiple datasets.
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.