RGB-Thermal Visual Place Recognition via Vision Foundation Model

Visual place recognition is a critical component of robust simultaneous localization and mapping systems. Conventional approaches primarily rely on RGB imagery, but their performance degrades significantly in extreme environments, such as those with poor illumination and airborne particulate interference (e.g., smoke or fog), which significantly degrade the performance of RGB-based methods. Furthermore, existing techniques often struggle with cross-scenario generalization. To overcome these limitations, we propose an RGB-thermal multimodal fusion framework for place recognition, specifically designed to enhance robustness in extreme environmental conditions. Our framework incorporates a dynamic RGB-thermal fusion module, coupled with dual fine-tuned vision foundation models as the feature extraction backbone. Experimental results on public datasets and our self-collected dataset demonstrate that our method significantly outperforms state-of-the-art RGB-based approaches, achieving generalizable and robust retrieval capabilities across day and night scenarios. The code is available at https://github.com/HITSZ-NRSL/RGB-Thermal-VPR. © 2025 IEEE.