TSCM: A Teacher-Student Model for Vision Place Recognition Using Cross-Metric Knowledge Distillation
This work addresses the problem of efficient and robust VPR for autonomous robots, offering an incremental improvement by enhancing model efficiency while maintaining performance.
The paper tackles the challenge of visual place recognition (VPR) in outdoor environments by proposing TSCM, a teacher-student distillation framework that uses cross-metric knowledge distillation to achieve high accuracy with minimal computational load, as demonstrated on Pittsburgh30k and Pittsburgh250k datasets.
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.