  Visual place recognition (VPR) plays a pivotal role in autonomous exploration
and navigation of mobile robots within complex outdoor environments. While
cost-effective and easily deployed, camera sensors are sensitive to lighting
and weather changes, and even slight image alterations can greatly affect VPR
efficiency and precision. Existing methods overcome this by exploiting powerful
yet large networks, leading to significant consumption of computational
resources. In this paper, we propose a high-performance teacher and lightweight
student distillation framework called TSCM. It exploits our devised
cross-metric knowledge distillation to narrow the performance gap between the
teacher and student models, maintaining superior performance while enabling
minimal computational load during deployment. We conduct comprehensive
evaluations on large-scale datasets, namely Pittsburgh30k and Pittsburgh250k.
Experimental results demonstrate the superiority of our method over baseline
models in terms of recognition accuracy and model parameter efficiency.
Moreover, our ablation studies show that the proposed knowledge distillation
technique surpasses other counterparts. The code of our method has been
released at https://github.com/nubot-nudt/TSCM.
