  Utilizing visual place recognition (VPR) technology to ascertain the
geographical location of publicly available images is a pressing issue for
real-world VPR applications. Although most current VPR methods achieve
favorable results under ideal conditions, their performance in complex
environments, characterized by lighting variations, seasonal changes, and
occlusions caused by moving objects, is generally unsatisfactory. In this
study, we utilize the DINOv2 model as the backbone network for trimming and
fine-tuning to extract robust image features. We propose a novel VPR
architecture called DINO-Mix, which combines a foundational vision model with
feature aggregation. This architecture relies on the powerful image feature
extraction capabilities of foundational vision models. We employ an
MLP-Mixer-based mix module to aggregate image features, resulting in globally
robust and generalizable descriptors that enable high-precision VPR. We
experimentally demonstrate that the proposed DINO-Mix architecture
significantly outperforms current state-of-the-art (SOTA) methods. In test sets
having lighting variations, seasonal changes, and occlusions (Tokyo24/7,
Nordland, SF-XL-Testv1), our proposed DINO-Mix architecture achieved Top-1
accuracy rates of 91.75%, 80.18%, and 82%, respectively. Compared with SOTA
methods, our architecture exhibited an average accuracy improvement of 5.14%.
