Abstract: Visual place recognition (VPR) is an important means for mobile robots to maintain high-precision localization and map consistency. However, due to the interference of viewpoint and appearance changes, the VPR problem remains extremely difficult. We propose a hierarchical VPR method based on joint decision-making of tightly coupled local and global descriptors. The proposed approach learns the ability to extract descriptors based on knowledge distillation. The well-trained lightweight model extracts global and local descriptors of an image in a tightly coupled form, further converts local descriptors into a binary representation, and maps it to the Bag of Visual Words space. In the constructed VPR architecture, a hierarchical recognition strategy is presented for coarse-to-fine place retrieval and a phase-correlation-based approach is employed to assign the joint decision weights of global and local descriptors. The evaluation results on several benchmark datasets confirm that the proposed approach achieves a significant improvement in performance with acceptable matching efficiency and exhibits strong generalization and robustness in various complex environments.